{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/GSA_PoP_Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Jun 2025, 11:54:08
{txt}
{com}. 
. 
. 
. *****************
. *** Load dataset
. *****************
. local drive "/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files"
{txt}
{com}. cd "`drive'"
{res}/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files
{txt}
{com}. use "GSA_PoP_TablesFigures.dta", clear
{txt}
{com}. 
. 
. 
. **********************************************************
. *** Figure 2: Approval of Recep Tayyip Erdogan, 2003-2021
. **********************************************************
. preserve
{txt}
{com}.         use "EAD 3.0 Annual_11282023.dta", clear
{txt}(Executive Approval Dataset 3.0 (Beta) July 2023 release: Annual)

{com}.         twoway line Approval_Not_Smoothed year if country_name=="Turkey_Pres", lcolor(black) ///
>                 || line Approval_Not_Smoothed year if country_name=="Turkey_PM" & year<2014 & year>=2003, lcolor(black) ///
>                 ytitle(Erdoğan Approval) ///
>                 ylab(30(10)80) ///
>                 xtitle(Year) ///
>                 xlab(2000(5)2025) ///
>                 xline(2003, lpattern(shortdash) lcolor(gs10)) ///
>                 xline(2007, lpattern(shortdash) lcolor(gs10)) ///
>                 xline(2011, lpattern(shortdash) lcolor(gs10)) ///
>                 xline(2014, lpattern(longdash) lcolor(gs10)) ///
>                 xline(2018, lpattern(longdash) lcolor(gs10)) ///
>                 xline(2023, lpattern(longdash) lcolor(gs10)) ///
>                 legend(pos(6) rows(1) order(2 "Prime Minister" 1 "President")) ///
>                 note("{c -(}it:Note{c )-}: Erdoğan became prime minister in 2003 following a by-election, and led the AKP to victories in the Grand National Assembly in 2007 and" "2011, as indicated by the short-dashed lines; he was then elected to the presidency in 2014, winning reelection in 2018 and 2023, as indicated""by the long dashed lines.", pos(7) size(vsmall)) ///
>                 scheme(plotplain)
{res}{txt}
{com}. restore
{txt}
{com}. 
. 
. 
. **************************************************************************
. *** Table 1: Summary of Average Treatment Effects on Approval for Erdogan
. **************************************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8, robust

{txt}Linear regression                               Number of obs     = {res}     3,839
                                                {txt}{help j_robustsingular:F(16, 3821) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0664
                                                {txt}Root MSE          =    {res} .38859

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0386804{col 26}{space 2}  .020216{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.0783155{col 67}{space 3} .0009547
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .007164{col 26}{space 2} .0198044{col 37}{space 1}    0.36{col 46}{space 3}0.718{col 54}{space 4}-.0316643{col 67}{space 3} .0459922
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} -.023032{col 26}{space 2}  .019885{col 37}{space 1}   -1.16{col 46}{space 3}0.247{col 54}{space 4}-.0620183{col 67}{space 3} .0159542
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0039108{col 26}{space 2} .0199158{col 37}{space 1}    0.20{col 46}{space 3}0.844{col 54}{space 4}-.0351358{col 67}{space 3} .0429575
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0192167{col 26}{space 2} .0129906{col 37}{space 1}    1.48{col 46}{space 3}0.139{col 54}{space 4}-.0062524{col 67}{space 3} .0446858
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0018441{col 26}{space 2} .0005467{col 37}{space 1}   -3.37{col 46}{space 3}0.001{col 54}{space 4} -.002916{col 67}{space 3}-.0007721
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0265398{col 26}{space 2} .0060839{col 37}{space 1}   -4.36{col 46}{space 3}0.000{col 54}{space 4}-.0384677{col 67}{space 3}-.0146119
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0248349{col 26}{space 2} .0170059{col 37}{space 1}   -1.46{col 46}{space 3}0.144{col 54}{space 4}-.0581763{col 67}{space 3} .0085066
{txt}{space 6}income {c |}{col 14}{res}{space 2} -.005004{col 26}{space 2} .0024964{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4}-.0098985{col 67}{space 3}-.0001096
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2795518{col 26}{space 2} .0171696{col 37}{space 1}   16.28{col 46}{space 3}0.000{col 54}{space 4} .2458892{col 67}{space 3} .3132144
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1288057{col 26}{space 2}  .022063{col 37}{space 1}   -5.84{col 46}{space 3}0.000{col 54}{space 4} -.172062{col 67}{space 3}-.0855493
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0465764{col 26}{space 2}  .026936{col 37}{space 1}   -1.73{col 46}{space 3}0.084{col 54}{space 4}-.0993867{col 67}{space 3} .0062338
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0259661{col 26}{space 2} .0232224{col 37}{space 1}   -1.12{col 46}{space 3}0.264{col 54}{space 4}-.0714956{col 67}{space 3} .0195634
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} -.003378{col 26}{space 2} .0292397{col 37}{space 1}   -0.12{col 46}{space 3}0.908{col 54}{space 4} -.060705{col 67}{space 3}  .053949
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0707863{col 26}{space 2} .0182282{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-.1065242{col 67}{space 3}-.0350484
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0463971{col 26}{space 2} .0237092{col 37}{space 1}   -1.96{col 46}{space 3}0.050{col 54}{space 4}-.0928809{col 67}{space 3} .0000868
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0335174{col 26}{space 2} .0281405{col 37}{space 1}   -1.19{col 46}{space 3}0.234{col 54}{space 4}-.0886893{col 67}{space 3} .0216545
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4463419{col 26}{space 2} .0457796{col 37}{space 1}    9.75{col 46}{space 3}0.000{col 54}{space 4}  .356587{col 67}{space 3} .5360967
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3839}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if order==2, robust

{txt}Linear regression                               Number of obs     = {res}     1,938
                                                {txt}{help j_robustsingular:F(16, 1920) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0710
                                                {txt}Root MSE          =    {res}   .388

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}  .005132{col 26}{space 2} .0287502{col 37}{space 1}    0.18{col 46}{space 3}0.858{col 54}{space 4}-.0512529{col 67}{space 3} .0615169
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0202224{col 26}{space 2} .0278934{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-.0344821{col 67}{space 3}  .074927
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0124666{col 26}{space 2} .0282508{col 37}{space 1}    0.44{col 46}{space 3}0.659{col 54}{space 4}-.0429388{col 67}{space 3}  .067872
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}   .00403{col 26}{space 2} .0279097{col 37}{space 1}    0.14{col 46}{space 3}0.885{col 54}{space 4}-.0507064{col 67}{space 3} .0587665
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0117127{col 26}{space 2} .0181553{col 37}{space 1}    0.65{col 46}{space 3}0.519{col 54}{space 4}-.0238936{col 67}{space 3}  .047319
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0010726{col 26}{space 2}  .000749{col 37}{space 1}   -1.43{col 46}{space 3}0.152{col 54}{space 4}-.0025416{col 67}{space 3} .0003964
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0207856{col 26}{space 2} .0086483{col 37}{space 1}   -2.40{col 46}{space 3}0.016{col 54}{space 4}-.0377465{col 67}{space 3}-.0038246
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0174483{col 26}{space 2} .0233177{col 37}{space 1}   -0.75{col 46}{space 3}0.454{col 54}{space 4}-.0631789{col 67}{space 3} .0282823
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0061806{col 26}{space 2}  .003477{col 37}{space 1}   -1.78{col 46}{space 3}0.076{col 54}{space 4}-.0129996{col 67}{space 3} .0006384
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2900803{col 26}{space 2} .0227916{col 37}{space 1}   12.73{col 46}{space 3}0.000{col 54}{space 4} .2453814{col 67}{space 3} .3347792
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1198683{col 26}{space 2} .0311512{col 37}{space 1}   -3.85{col 46}{space 3}0.000{col 54}{space 4} -.180962{col 67}{space 3}-.0587747
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} -.032791{col 26}{space 2} .0382227{col 37}{space 1}   -0.86{col 46}{space 3}0.391{col 54}{space 4}-.1077533{col 67}{space 3} .0421713
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0017427{col 26}{space 2} .0329879{col 37}{space 1}   -0.05{col 46}{space 3}0.958{col 54}{space 4}-.0664386{col 67}{space 3} .0629532
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0109477{col 26}{space 2} .0441451{col 37}{space 1}   -0.25{col 46}{space 3}0.804{col 54}{space 4}-.0975252{col 67}{space 3} .0756297
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0753255{col 26}{space 2} .0254864{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.1253095{col 67}{space 3}-.0253415
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0681132{col 26}{space 2} .0328141{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-.1324682{col 67}{space 3}-.0037582
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0110898{col 26}{space 2} .0391511{col 37}{space 1}    0.28{col 46}{space 3}0.777{col 54}{space 4}-.0656933{col 67}{space 3} .0878728
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3814286{col 26}{space 2} .0645226{col 37}{space 1}    5.91{col 46}{space 3}0.000{col 54}{space 4} .2548868{col 67}{space 3} .5079704
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1938}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if order==1, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,901
                                                {txt}F(16, 1884)       =  {res}    12.32
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0721
                                                {txt}Root MSE          =    {res} .38857

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} -.083477{col 26}{space 2} .0285262{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.1394231{col 67}{space 3}-.0275308
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0053059{col 26}{space 2} .0282556{col 37}{space 1}   -0.19{col 46}{space 3}0.851{col 54}{space 4}-.0607216{col 67}{space 3} .0501097
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0584508{col 26}{space 2} .0279217{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.1132116{col 67}{space 3}-.0036901
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0028792{col 26}{space 2} .0285697{col 37}{space 1}    0.10{col 46}{space 3}0.920{col 54}{space 4}-.0531525{col 67}{space 3} .0589108
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0259736{col 26}{space 2} .0186108{col 37}{space 1}    1.40{col 46}{space 3}0.163{col 54}{space 4}-.0105263{col 67}{space 3} .0624735
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0028516{col 26}{space 2} .0007965{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0044138{col 67}{space 3}-.0012894
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.032857{col 26}{space 2} .0085749{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.0496742{col 67}{space 3}-.0160397
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0312024{col 26}{space 2} .0251338{col 37}{space 1}   -1.24{col 46}{space 3}0.215{col 54}{space 4}-.0804954{col 67}{space 3} .0180905
{txt}{space 6}income {c |}{col 14}{res}{space 2}  -.00362{col 26}{space 2} .0035927{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}-.0106661{col 67}{space 3} .0034262
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2701949{col 26}{space 2}  .026042{col 37}{space 1}   10.38{col 46}{space 3}0.000{col 54}{space 4} .2191207{col 67}{space 3}  .321269
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1361741{col 26}{space 2} .0405958{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4}-.2157917{col 67}{space 3}-.0565566
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0646129{col 26}{space 2} .0466803{col 37}{space 1}   -1.38{col 46}{space 3}0.166{col 54}{space 4}-.1561634{col 67}{space 3} .0269376
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0540477{col 26}{space 2} .0417237{col 37}{space 1}   -1.30{col 46}{space 3}0.195{col 54}{space 4}-.1358771{col 67}{space 3} .0277817
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0685149{col 26}{space 2} .0373185{col 37}{space 1}   -1.84{col 46}{space 3}0.067{col 54}{space 4}-.1417048{col 67}{space 3} .0046749
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0253662{col 26}{space 2} .0432731{col 37}{space 1}   -0.59{col 46}{space 3}0.558{col 54}{space 4}-.1102344{col 67}{space 3} .0595021
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0827082{col 26}{space 2} .0477035{col 37}{space 1}   -1.73{col 46}{space 3}0.083{col 54}{space 4}-.1762655{col 67}{space 3} .0108491
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .521944{col 26}{space 2} .0671329{col 37}{space 1}    7.77{col 46}{space 3}0.000{col 54}{space 4} .3902812{col 67}{space 3} .6536067
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1901}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. esttab using "`drive'/ATEreduced.tex", replace ///
>         keep(t2 t3 t4 t5 _cons) ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(r2 controls sample i, label("R2" "Controls" "Sample" "Observations")) ///
>         title("Summary of Average Treatment Effects on Approval for Erdoğan \label{c -(}tab:ATEreduced{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/ATEreduced.tex"'})

{com}. 
. 
. 
. **********************************************************
. *** Figure 3: Predictive Margins for the Primed Treatment
. **********************************************************      
. reg o1_std i.treatment female age education govt_emp income islam r2-r8 if order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,901
                                                {txt}F(16, 1884)       =  {res}    12.32
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0721
                                                {txt}Root MSE          =    {res} .38857

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}
{space 6}Force  {c |}{col 14}{res}{space 2} -.083477{col 26}{space 2} .0285262{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.1394231{col 67}{space 3}-.0275308
{txt}{space 3}Minister  {c |}{col 14}{res}{space 2}-.0053059{col 26}{space 2} .0282556{col 37}{space 1}   -0.19{col 46}{space 3}0.851{col 54}{space 4}-.0607216{col 67}{space 3} .0501097
{txt}{space 1}Opposition  {c |}{col 14}{res}{space 2}-.0584508{col 26}{space 2} .0279217{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.1132116{col 67}{space 3}-.0036901
{txt}{space 4}Private  {c |}{col 14}{res}{space 2} .0028792{col 26}{space 2} .0285697{col 37}{space 1}    0.10{col 46}{space 3}0.920{col 54}{space 4}-.0531525{col 67}{space 3} .0589108
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0259736{col 26}{space 2} .0186108{col 37}{space 1}    1.40{col 46}{space 3}0.163{col 54}{space 4}-.0105263{col 67}{space 3} .0624735
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0028516{col 26}{space 2} .0007965{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0044138{col 67}{space 3}-.0012894
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.032857{col 26}{space 2} .0085749{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.0496742{col 67}{space 3}-.0160397
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0312024{col 26}{space 2} .0251338{col 37}{space 1}   -1.24{col 46}{space 3}0.215{col 54}{space 4}-.0804954{col 67}{space 3} .0180905
{txt}{space 6}income {c |}{col 14}{res}{space 2}  -.00362{col 26}{space 2} .0035927{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}-.0106661{col 67}{space 3} .0034262
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2701949{col 26}{space 2}  .026042{col 37}{space 1}   10.38{col 46}{space 3}0.000{col 54}{space 4} .2191207{col 67}{space 3}  .321269
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0534659{col 26}{space 2} .0407342{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4}-.1333549{col 67}{space 3}  .026423
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0180953{col 26}{space 2} .0468832{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0738532{col 67}{space 3} .1100438
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0286605{col 26}{space 2} .0419543{col 37}{space 1}    0.68{col 46}{space 3}0.495{col 54}{space 4}-.0536213{col 67}{space 3} .1109423
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0827082{col 26}{space 2} .0477035{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0108491{col 67}{space 3} .1762655
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0141932{col 26}{space 2} .0375238{col 37}{space 1}    0.38{col 46}{space 3}0.705{col 54}{space 4}-.0593994{col 67}{space 3} .0877859
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}  .057342{col 26}{space 2}  .043461{col 37}{space 1}    1.32{col 46}{space 3}0.187{col 54}{space 4}-.0278947{col 67}{space 3} .1425788
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .4392358{col 26}{space 2} .0663331{col 37}{space 1}    6.62{col 46}{space 3}0.000{col 54}{space 4} .3091417{col 67}{space 3} .5693298
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins i.treatment, level(84)
{res}
{txt}{col 1}Predictive margins{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,901}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}
{space 4}Control  {c |}{col 14}{res}{space 2} .4317476{col 26}{space 2} .0200595{col 37}{space 1}   21.52{col 46}{space 3}0.000{col 54}{space 4} .4035514{col 67}{space 3} .4599437
{txt}{space 6}Force  {c |}{col 14}{res}{space 2} .3482706{col 26}{space 2} .0202294{col 37}{space 1}   17.22{col 46}{space 3}0.000{col 54}{space 4} .3198356{col 67}{space 3} .3767056
{txt}{space 3}Minister  {c |}{col 14}{res}{space 2} .4264416{col 26}{space 2} .0198695{col 37}{space 1}   21.46{col 46}{space 3}0.000{col 54}{space 4} .3985125{col 67}{space 3} .4543708
{txt}{space 1}Opposition  {c |}{col 14}{res}{space 2} .3732967{col 26}{space 2} .0194402{col 37}{space 1}   19.20{col 46}{space 3}0.000{col 54}{space 4} .3459711{col 67}{space 3} .4006224
{txt}{space 4}Private  {c |}{col 14}{res}{space 2} .4346267{col 26}{space 2}  .020331{col 37}{space 1}   21.38{col 46}{space 3}0.000{col 54}{space 4} .4060489{col 67}{space 3} .4632046
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         recast(scatter) ///
>         scheme(plotplain) ///
>         yscale(range (0 .6)) ylabel(0(.2).6) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         xtitle("Control or Treatment Groups") xlabel(1 "Control" 2 `""Force" "majeure""' 3 "Minister" 4 "Opposition" 5 `""Private" "companies""') ///
>         title("") ///
>         name(ATEprimed, replace) ///
>         graphregion(margin(5 10 5 10))
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:treatment}{p_end}
{res}{txt}
{com}. 
. 
. 
. ****************************************************************
. *** Figure 4: Primed Treatment Effects Conditional on Education
. ****************************************************************
. preserve
{txt}
{com}. keep if order==1
{txt}(2,494 observations deleted)

{com}. reg o1_std i.t2##c.education female age education govt_emp income islam r2-r8 if treatment==1 | treatment==2, robust
{txt}{p 0 6 2}note: {bf:education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       752
                                                {txt}F(14, 737)        =  {res}     5.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0732
                                                {txt}Root MSE          =    {res} .38974

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t2 {c |}{col 16}{res}{space 2}-.0342147{col 28}{space 2} .1111706{col 39}{space 1}   -0.31{col 48}{space 3}0.758{col 56}{space 4}-.2524636{col 69}{space 3} .1840341
{txt}{space 5}education {c |}{col 16}{res}{space 2} -.016802{col 28}{space 2} .0194166{col 39}{space 1}   -0.87{col 48}{space 3}0.387{col 56}{space 4}-.0549204{col 69}{space 3} .0213165
{txt}{space 14} {c |}
t2#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0113398{col 28}{space 2} .0250172{col 39}{space 1}   -0.45{col 48}{space 3}0.650{col 56}{space 4}-.0604532{col 69}{space 3} .0377736
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0352002{col 28}{space 2}  .030221{col 39}{space 1}    1.16{col 48}{space 3}0.244{col 56}{space 4}-.0241293{col 69}{space 3} .0945296
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0030533{col 28}{space 2} .0012646{col 39}{space 1}   -2.41{col 48}{space 3}0.016{col 56}{space 4}-.0055359{col 69}{space 3}-.0005707
{txt}{space 5}education {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 6}govt_emp {c |}{col 16}{res}{space 2} .0111956{col 28}{space 2}  .039652{col 39}{space 1}    0.28{col 48}{space 3}0.778{col 56}{space 4}-.0666486{col 69}{space 3} .0890399
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0020102{col 28}{space 2} .0057544{col 39}{space 1}   -0.35{col 48}{space 3}0.727{col 56}{space 4}-.0133072{col 69}{space 3} .0092868
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2597857{col 28}{space 2} .0409339{col 39}{space 1}    6.35{col 48}{space 3}0.000{col 56}{space 4} .1794248{col 69}{space 3} .3401467
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0012975{col 28}{space 2}  .062563{col 39}{space 1}   -0.02{col 48}{space 3}0.983{col 56}{space 4}-.1241204{col 69}{space 3} .1215253
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .1531485{col 28}{space 2} .0723252{col 39}{space 1}    2.12{col 48}{space 3}0.035{col 56}{space 4} .0111606{col 69}{space 3} .2951363
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0857552{col 28}{space 2} .0667445{col 39}{space 1}    1.28{col 48}{space 3}0.199{col 56}{space 4}-.0452768{col 69}{space 3} .2167872
{txt}{space 12}r5 {c |}{col 16}{res}{space 2}  .124714{col 28}{space 2}  .081051{col 39}{space 1}    1.54{col 48}{space 3}0.124{col 56}{space 4}-.0344044{col 69}{space 3} .2838323
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0665067{col 28}{space 2} .0589822{col 39}{space 1}    1.13{col 48}{space 3}0.260{col 56}{space 4}-.0492864{col 69}{space 3} .1822998
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0724407{col 28}{space 2} .0674373{col 39}{space 1}    1.07{col 48}{space 3}0.283{col 56}{space 4}-.0599513{col 69}{space 3} .2048328
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3111332{col 28}{space 2} .1177505{col 39}{space 1}    2.64{col 48}{space 3}0.008{col 56}{space 4} .0799669{col 69}{space 3} .5422996
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t2, at(education=(0(1)5)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:752}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 9:education} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 9:education} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 9:education} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 9:education} = {res:{ralign 1:3}}
{lalign 7:5._at: }{space 0}{lalign 9:education} = {res:{ralign 1:4}}
{lalign 7:6._at: }{space 0}{lalign 9:education} = {res:{ralign 1:5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t2 {c |}
{space 8}1 0  {c |}{col 14}{res}{space 2} .5043188{col 26}{space 2} .0857556{col 37}{space 1}    5.88{col 46}{space 3}0.000{col 54}{space 4} .3837044{col 67}{space 3} .6249332
{txt}{space 8}1 1  {c |}{col 14}{res}{space 2} .4701041{col 26}{space 2} .0801914{col 37}{space 1}    5.86{col 46}{space 3}0.000{col 54}{space 4} .3573156{col 67}{space 3} .5828926
{txt}{space 8}2 0  {c |}{col 14}{res}{space 2} .4875169{col 26}{space 2} .0670255{col 37}{space 1}    7.27{col 46}{space 3}0.000{col 54}{space 4} .3932461{col 67}{space 3} .5817877
{txt}{space 8}2 1  {c |}{col 14}{res}{space 2} .4419623{col 26}{space 2} .0628793{col 37}{space 1}    7.03{col 46}{space 3}0.000{col 54}{space 4} .3535232{col 67}{space 3} .5304014
{txt}{space 8}3 0  {c |}{col 14}{res}{space 2} .4707149{col 26}{space 2} .0488348{col 37}{space 1}    9.64{col 46}{space 3}0.000{col 54}{space 4} .4020292{col 67}{space 3} .5394006
{txt}{space 8}3 1  {c |}{col 14}{res}{space 2} .4138205{col 26}{space 2} .0461555{col 37}{space 1}    8.97{col 46}{space 3}0.000{col 54}{space 4} .3489032{col 67}{space 3} .4787378
{txt}{space 8}4 0  {c |}{col 14}{res}{space 2} .4539129{col 26}{space 2} .0321133{col 37}{space 1}   14.13{col 46}{space 3}0.000{col 54}{space 4} .4087459{col 67}{space 3}   .49908
{txt}{space 8}4 1  {c |}{col 14}{res}{space 2} .3856787{col 26}{space 2} .0309878{col 37}{space 1}   12.45{col 46}{space 3}0.000{col 54}{space 4} .3420947{col 67}{space 3} .4292627
{txt}{space 8}5 0  {c |}{col 14}{res}{space 2}  .437111{col 26}{space 2} .0207773{col 37}{space 1}   21.04{col 46}{space 3}0.000{col 54}{space 4} .4078878{col 67}{space 3} .4663341
{txt}{space 8}5 1  {c |}{col 14}{res}{space 2} .3575369{col 26}{space 2} .0210604{col 37}{space 1}   16.98{col 46}{space 3}0.000{col 54}{space 4} .3279157{col 67}{space 3} .3871581
{txt}{space 8}6 0  {c |}{col 14}{res}{space 2}  .420309{col 26}{space 2} .0242104{col 37}{space 1}   17.36{col 46}{space 3}0.000{col 54}{space 4} .3862573{col 67}{space 3} .4543607
{txt}{space 8}6 1  {c |}{col 14}{res}{space 2} .3293951{col 26}{space 2} .0240878{col 37}{space 1}   13.67{col 46}{space 3}0.000{col 54}{space 4} .2955158{col 67}{space 3} .3632744
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).8) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Force majeure") ///
>         xtitle("") xlabel(0 `""Primary""school""' 1 `""Middle""school""' 2 `""High""school""' 3 `""College""diploma""' 4 `""Bachlor's""degree""' 5 `""Master's""or higher""', labsize(vsmall)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETeducationt2, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:education t2}{p_end}
{res}{txt}
{com}. reg o1_std i.t3##c.education female age education govt_emp income islam r2-r8 if treatment==1 | treatment==3, robust
{txt}{p 0 6 2}note: {bf:education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       768
                                                {txt}F(14, 753)        =  {res}     5.33
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0735
                                                {txt}Root MSE          =    {res} .38984

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t3 {c |}{col 16}{res}{space 2} .0748779{col 28}{space 2} .1170449{col 39}{space 1}    0.64{col 48}{space 3}0.523{col 56}{space 4}-.1548952{col 69}{space 3}  .304651
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0210484{col 28}{space 2} .0193492{col 39}{space 1}   -1.09{col 48}{space 3}0.277{col 56}{space 4}-.0590332{col 69}{space 3} .0169364
{txt}{space 14} {c |}
t3#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0188551{col 28}{space 2} .0257091{col 39}{space 1}   -0.73{col 48}{space 3}0.464{col 56}{space 4}-.0693252{col 69}{space 3} .0316151
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0142134{col 28}{space 2}  .029625{col 39}{space 1}    0.48{col 48}{space 3}0.632{col 56}{space 4}-.0439441{col 69}{space 3} .0723708
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0032802{col 28}{space 2} .0013007{col 39}{space 1}   -2.52{col 48}{space 3}0.012{col 56}{space 4}-.0058335{col 69}{space 3}-.0007268
{txt}{space 5}education {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0042367{col 28}{space 2} .0419285{col 39}{space 1}   -0.10{col 48}{space 3}0.920{col 56}{space 4}-.0865474{col 69}{space 3}  .078074
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0021918{col 28}{space 2} .0058216{col 39}{space 1}    0.38{col 48}{space 3}0.707{col 56}{space 4}-.0092367{col 69}{space 3} .0136203
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2869577{col 28}{space 2} .0413409{col 39}{space 1}    6.94{col 48}{space 3}0.000{col 56}{space 4} .2058006{col 69}{space 3} .3681148
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0386567{col 28}{space 2} .0643129{col 39}{space 1}   -0.60{col 48}{space 3}0.548{col 56}{space 4}-.1649106{col 69}{space 3} .0875973
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0787004{col 28}{space 2} .0719918{col 39}{space 1}    1.09{col 48}{space 3}0.275{col 56}{space 4}-.0626281{col 69}{space 3} .2200288
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .1046004{col 28}{space 2}  .066438{col 39}{space 1}    1.57{col 48}{space 3}0.116{col 56}{space 4}-.0258253{col 69}{space 3} .2350261
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .0649983{col 28}{space 2} .0759011{col 39}{space 1}    0.86{col 48}{space 3}0.392{col 56}{space 4}-.0840046{col 69}{space 3} .2140012
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0524954{col 28}{space 2} .0592043{col 39}{space 1}    0.89{col 48}{space 3}0.376{col 56}{space 4}-.0637298{col 69}{space 3} .1687206
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0272104{col 28}{space 2} .0671176{col 39}{space 1}    0.41{col 48}{space 3}0.685{col 56}{space 4}-.1045494{col 69}{space 3} .1589702
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3191254{col 28}{space 2}  .119018{col 39}{space 1}    2.68{col 48}{space 3}0.007{col 56}{space 4} .0854788{col 69}{space 3} .5527719
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t3, at(education=(0(1)5)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:768}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 9:education} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 9:education} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 9:education} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 9:education} = {res:{ralign 1:3}}
{lalign 7:5._at: }{space 0}{lalign 9:education} = {res:{ralign 1:4}}
{lalign 7:6._at: }{space 0}{lalign 9:education} = {res:{ralign 1:5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t3 {c |}
{space 8}1 0  {c |}{col 14}{res}{space 2} .5214906{col 26}{space 2} .0858524{col 37}{space 1}    6.07{col 46}{space 3}0.000{col 54}{space 4} .4007426{col 67}{space 3} .6422386
{txt}{space 8}1 1  {c |}{col 14}{res}{space 2} .5963685{col 26}{space 2} .0861845{col 37}{space 1}    6.92{col 46}{space 3}0.000{col 54}{space 4} .4751534{col 67}{space 3} .7175836
{txt}{space 8}2 0  {c |}{col 14}{res}{space 2} .5004422{col 26}{space 2} .0671831{col 37}{space 1}    7.45{col 46}{space 3}0.000{col 54}{space 4} .4059518{col 67}{space 3} .5949327
{txt}{space 8}2 1  {c |}{col 14}{res}{space 2} .5564651{col 26}{space 2} .0681596{col 37}{space 1}    8.16{col 46}{space 3}0.000{col 54}{space 4} .4606013{col 67}{space 3} .6523289
{txt}{space 8}3 0  {c |}{col 14}{res}{space 2} .4793939{col 26}{space 2} .0490438{col 37}{space 1}    9.77{col 46}{space 3}0.000{col 54}{space 4} .4104157{col 67}{space 3}  .548372
{txt}{space 8}3 1  {c |}{col 14}{res}{space 2} .5165617{col 26}{space 2} .0506044{col 37}{space 1}   10.21{col 46}{space 3}0.000{col 54}{space 4} .4453886{col 67}{space 3} .5877347
{txt}{space 8}4 0  {c |}{col 14}{res}{space 2} .4583455{col 26}{space 2} .0323388{col 37}{space 1}   14.17{col 46}{space 3}0.000{col 54}{space 4} .4128623{col 67}{space 3} .5038287
{txt}{space 8}4 1  {c |}{col 14}{res}{space 2} .4766582{col 26}{space 2} .0342487{col 37}{space 1}   13.92{col 46}{space 3}0.000{col 54}{space 4} .4284888{col 67}{space 3} .5248276
{txt}{space 8}5 0  {c |}{col 14}{res}{space 2} .4372971{col 26}{space 2} .0208587{col 37}{space 1}   20.96{col 46}{space 3}0.000{col 54}{space 4} .4079602{col 67}{space 3}  .466634
{txt}{space 8}5 1  {c |}{col 14}{res}{space 2} .4367548{col 26}{space 2}   .02196{col 37}{space 1}   19.89{col 46}{space 3}0.000{col 54}{space 4}  .405869{col 67}{space 3} .4676407
{txt}{space 8}6 0  {c |}{col 14}{res}{space 2} .4162487{col 26}{space 2} .0239406{col 37}{space 1}   17.39{col 46}{space 3}0.000{col 54}{space 4} .3825772{col 67}{space 3} .4499203
{txt}{space 8}6 1  {c |}{col 14}{res}{space 2} .3968514{col 26}{space 2} .0221046{col 37}{space 1}   17.95{col 46}{space 3}0.000{col 54}{space 4} .3657622{col 67}{space 3} .4279406
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).8) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Minister") ///
>         xtitle("") xlabel(0 `""Primary""school""' 1 `""Middle""school""' 2 `""High""school""' 3 `""College""diploma""' 4 `""Bachlor's""degree""' 5 `""Master's""or higher""', labsize(vsmall)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETeducationt3, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:education t3}{p_end}
{res}{txt}
{com}. reg o1_std i.t4##c.education female age education govt_emp income islam r2-r8 if treatment==1 | treatment==4, robust
{txt}{p 0 6 2}note: {bf:education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       762
                                                {txt}F(14, 747)        =  {res}     9.25
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1100
                                                {txt}Root MSE          =    {res} .38335

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t4 {c |}{col 16}{res}{space 2} .0980835{col 28}{space 2} .1098361{col 39}{space 1}    0.89{col 48}{space 3}0.372{col 56}{space 4}-.1175407{col 69}{space 3} .3137077
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0214104{col 28}{space 2} .0194818{col 39}{space 1}   -1.10{col 48}{space 3}0.272{col 56}{space 4}-.0596559{col 69}{space 3} .0168351
{txt}{space 14} {c |}
t4#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0367267{col 28}{space 2} .0247221{col 39}{space 1}   -1.49{col 48}{space 3}0.138{col 56}{space 4}-.0852597{col 69}{space 3} .0118064
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0473659{col 28}{space 2} .0291255{col 39}{space 1}    1.63{col 48}{space 3}0.104{col 56}{space 4}-.0098117{col 69}{space 3} .1045435
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0044478{col 28}{space 2} .0012458{col 39}{space 1}   -3.57{col 48}{space 3}0.000{col 56}{space 4}-.0068934{col 69}{space 3}-.0020022
{txt}{space 5}education {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0261689{col 28}{space 2} .0395354{col 39}{space 1}   -0.66{col 48}{space 3}0.508{col 56}{space 4}-.1037826{col 69}{space 3} .0514448
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0004108{col 28}{space 2} .0056248{col 39}{space 1}   -0.07{col 48}{space 3}0.942{col 56}{space 4}-.0114531{col 69}{space 3} .0106314
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .3011966{col 28}{space 2} .0380615{col 39}{space 1}    7.91{col 48}{space 3}0.000{col 56}{space 4} .2264764{col 69}{space 3} .3759169
{txt}{space 12}r2 {c |}{col 16}{res}{space 2} .0068977{col 28}{space 2} .0675753{col 39}{space 1}    0.10{col 48}{space 3}0.919{col 56}{space 4}-.1257625{col 69}{space 3} .1395579
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .1441094{col 28}{space 2} .0741019{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.0013633{col 69}{space 3} .2895821
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .1203726{col 28}{space 2} .0694152{col 39}{space 1}    1.73{col 48}{space 3}0.083{col 56}{space 4}-.0158994{col 69}{space 3} .2566447
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .2067239{col 28}{space 2} .0737967{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0618504{col 69}{space 3} .3515975
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0715855{col 28}{space 2} .0614699{col 39}{space 1}    1.16{col 48}{space 3}0.245{col 56}{space 4}-.0490888{col 69}{space 3} .1922598
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0868557{col 28}{space 2} .0696787{col 39}{space 1}    1.25{col 48}{space 3}0.213{col 56}{space 4}-.0499337{col 69}{space 3} .2236451
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3218703{col 28}{space 2} .1178116{col 39}{space 1}    2.73{col 48}{space 3}0.006{col 56}{space 4}  .090589{col 69}{space 3} .5531516
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t4, at(education=(0(1)5)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:762}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 9:education} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 9:education} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 9:education} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 9:education} = {res:{ralign 1:3}}
{lalign 7:5._at: }{space 0}{lalign 9:education} = {res:{ralign 1:4}}
{lalign 7:6._at: }{space 0}{lalign 9:education} = {res:{ralign 1:5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t4 {c |}
{space 8}1 0  {c |}{col 14}{res}{space 2} .5237522{col 26}{space 2} .0864452{col 37}{space 1}    6.06{col 46}{space 3}0.000{col 54}{space 4} .4021695{col 67}{space 3} .6453349
{txt}{space 8}1 1  {c |}{col 14}{res}{space 2} .6218357{col 26}{space 2} .0766389{col 37}{space 1}    8.11{col 46}{space 3}0.000{col 54}{space 4} .5140453{col 67}{space 3} .7296261
{txt}{space 8}2 0  {c |}{col 14}{res}{space 2} .5023418{col 26}{space 2}  .067635{col 37}{space 1}    7.43{col 46}{space 3}0.000{col 54}{space 4}  .407215{col 67}{space 3} .5974685
{txt}{space 8}2 1  {c |}{col 14}{res}{space 2} .5636986{col 26}{space 2} .0600527{col 37}{space 1}    9.39{col 46}{space 3}0.000{col 54}{space 4} .4792362{col 67}{space 3}  .648161
{txt}{space 8}3 0  {c |}{col 14}{res}{space 2} .4809313{col 26}{space 2} .0493488{col 37}{space 1}    9.75{col 46}{space 3}0.000{col 54}{space 4} .4115237{col 67}{space 3} .5503389
{txt}{space 8}3 1  {c |}{col 14}{res}{space 2} .5055615{col 26}{space 2} .0440366{col 37}{space 1}   11.48{col 46}{space 3}0.000{col 54}{space 4} .4436253{col 67}{space 3} .5674976
{txt}{space 8}4 0  {c |}{col 14}{res}{space 2} .4595209{col 26}{space 2} .0324835{col 37}{space 1}   14.15{col 46}{space 3}0.000{col 54}{space 4} .4138337{col 67}{space 3} .5052081
{txt}{space 8}4 1  {c |}{col 14}{res}{space 2} .4474244{col 26}{space 2} .0295331{col 37}{space 1}   15.15{col 46}{space 3}0.000{col 54}{space 4}  .405887{col 67}{space 3} .4889618
{txt}{space 8}5 0  {c |}{col 14}{res}{space 2} .4381104{col 26}{space 2}  .020836{col 37}{space 1}   21.03{col 46}{space 3}0.000{col 54}{space 4} .4088052{col 67}{space 3} .4674157
{txt}{space 8}5 1  {c |}{col 14}{res}{space 2} .3892873{col 26}{space 2} .0201314{col 37}{space 1}   19.34{col 46}{space 3}0.000{col 54}{space 4}  .360973{col 67}{space 3} .4176015
{txt}{space 8}6 0  {c |}{col 14}{res}{space 2}    .4167{col 26}{space 2} .0239202{col 37}{space 1}   17.42{col 46}{space 3}0.000{col 54}{space 4} .3830569{col 67}{space 3} .4503431
{txt}{space 8}6 1  {c |}{col 14}{res}{space 2} .3311502{col 26}{space 2} .0232041{col 37}{space 1}   14.27{col 46}{space 3}0.000{col 54}{space 4} .2985142{col 67}{space 3} .3637861
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).8) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Opposition") ///
>         xtitle("") xlabel(0 `""Primary""school""' 1 `""Middle""school""' 2 `""High""school""' 3 `""College""diploma""' 4 `""Bachlor's""degree""' 5 `""Master's""or higher""', labsize(vsmall)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETeducationt4, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:education t4}{p_end}
{res}{txt}
{com}. reg o1_std i.t5##c.education female age education govt_emp income islam r2-r8 if treatment==1 | treatment==5, robust
{txt}{p 0 6 2}note: {bf:education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       756
                                                {txt}F(14, 741)        =  {res}     6.53
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0809
                                                {txt}Root MSE          =    {res} .39212

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t5 {c |}{col 16}{res}{space 2} .0108669{col 28}{space 2} .1174487{col 39}{space 1}    0.09{col 48}{space 3}0.926{col 56}{space 4} -.219705{col 69}{space 3} .2414388
{txt}{space 5}education {c |}{col 16}{res}{space 2} -.017824{col 28}{space 2} .0194833{col 39}{space 1}   -0.91{col 48}{space 3}0.361{col 56}{space 4} -.056073{col 69}{space 3} .0204251
{txt}{space 14} {c |}
t5#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0022301{col 28}{space 2} .0259462{col 39}{space 1}   -0.09{col 48}{space 3}0.932{col 56}{space 4}-.0531669{col 69}{space 3} .0487067
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0342465{col 28}{space 2} .0298401{col 39}{space 1}    1.15{col 48}{space 3}0.251{col 56}{space 4}-.0243346{col 69}{space 3} .0928277
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0026543{col 28}{space 2} .0012879{col 39}{space 1}   -2.06{col 48}{space 3}0.040{col 56}{space 4}-.0051827{col 69}{space 3}-.0001259
{txt}{space 5}education {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0226835{col 28}{space 2} .0412286{col 39}{space 1}   -0.55{col 48}{space 3}0.582{col 56}{space 4}-.1036222{col 69}{space 3} .0582552
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0051802{col 28}{space 2} .0057371{col 39}{space 1}   -0.90{col 48}{space 3}0.367{col 56}{space 4}-.0164431{col 69}{space 3} .0060827
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2998128{col 28}{space 2}  .043526{col 39}{space 1}    6.89{col 48}{space 3}0.000{col 56}{space 4} .2143638{col 69}{space 3} .3852618
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0399892{col 28}{space 2} .0645381{col 39}{space 1}   -0.62{col 48}{space 3}0.536{col 56}{space 4}-.1666886{col 69}{space 3} .0867102
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0814878{col 28}{space 2}  .073093{col 39}{space 1}    1.11{col 48}{space 3}0.265{col 56}{space 4}-.0620064{col 69}{space 3} .2249819
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .1331404{col 28}{space 2} .0695585{col 39}{space 1}    1.91{col 48}{space 3}0.056{col 56}{space 4}-.0034147{col 69}{space 3} .2696955
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .1961361{col 28}{space 2} .0797602{col 39}{space 1}    2.46{col 48}{space 3}0.014{col 56}{space 4} .0395532{col 69}{space 3}  .352719
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0610197{col 28}{space 2} .0606138{col 39}{space 1}    1.01{col 48}{space 3}0.314{col 56}{space 4}-.0579756{col 69}{space 3}  .180015
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0635115{col 28}{space 2} .0678099{col 39}{space 1}    0.94{col 48}{space 3}0.349{col 56}{space 4}-.0696108{col 69}{space 3} .1966339
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3002075{col 28}{space 2} .1197797{col 39}{space 1}    2.51{col 48}{space 3}0.012{col 56}{space 4} .0650596{col 69}{space 3} .5353555
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t5, at(education=(0(1)5)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:756}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 9:education} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 9:education} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 9:education} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 9:education} = {res:{ralign 1:3}}
{lalign 7:5._at: }{space 0}{lalign 9:education} = {res:{ralign 1:4}}
{lalign 7:6._at: }{space 0}{lalign 9:education} = {res:{ralign 1:5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t5 {c |}
{space 8}1 0  {c |}{col 14}{res}{space 2} .5095488{col 26}{space 2} .0863941{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4}  .388037{col 67}{space 3} .6310606
{txt}{space 8}1 1  {c |}{col 14}{res}{space 2} .5204157{col 26}{space 2} .0857725{col 37}{space 1}    6.07{col 46}{space 3}0.000{col 54}{space 4} .3997781{col 67}{space 3} .6410533
{txt}{space 8}2 0  {c |}{col 14}{res}{space 2} .4917248{col 26}{space 2}  .067587{col 37}{space 1}    7.28{col 46}{space 3}0.000{col 54}{space 4} .3966648{col 67}{space 3} .5867848
{txt}{space 8}2 1  {c |}{col 14}{res}{space 2} .5003616{col 26}{space 2} .0678021{col 37}{space 1}    7.38{col 46}{space 3}0.000{col 54}{space 4}  .404999{col 67}{space 3} .5957241
{txt}{space 8}3 0  {c |}{col 14}{res}{space 2} .4739008{col 26}{space 2} .0493079{col 37}{space 1}    9.61{col 46}{space 3}0.000{col 54}{space 4} .4045501{col 67}{space 3} .5432515
{txt}{space 8}3 1  {c |}{col 14}{res}{space 2} .4803075{col 26}{space 2} .0503308{col 37}{space 1}    9.54{col 46}{space 3}0.000{col 54}{space 4} .4095181{col 67}{space 3} .5510969
{txt}{space 8}4 0  {c |}{col 14}{res}{space 2} .4560768{col 26}{space 2} .0324612{col 37}{space 1}   14.05{col 46}{space 3}0.000{col 54}{space 4} .4104206{col 67}{space 3}  .501733
{txt}{space 8}4 1  {c |}{col 14}{res}{space 2} .4602534{col 26}{space 2} .0341336{col 37}{space 1}   13.48{col 46}{space 3}0.000{col 54}{space 4} .4122451{col 67}{space 3} .5082617
{txt}{space 8}5 0  {c |}{col 14}{res}{space 2} .4382528{col 26}{space 2} .0208661{col 37}{space 1}   21.00{col 46}{space 3}0.000{col 54}{space 4}  .408905{col 67}{space 3} .4676006
{txt}{space 8}5 1  {c |}{col 14}{res}{space 2} .4401993{col 26}{space 2} .0222006{col 37}{space 1}   19.83{col 46}{space 3}0.000{col 54}{space 4} .4089745{col 67}{space 3} .4714242
{txt}{space 8}6 0  {c |}{col 14}{res}{space 2} .4204288{col 26}{space 2} .0240053{col 37}{space 1}   17.51{col 46}{space 3}0.000{col 54}{space 4} .3866659{col 67}{space 3} .4541918
{txt}{space 8}6 1  {c |}{col 14}{res}{space 2} .4201452{col 26}{space 2} .0227265{col 37}{space 1}   18.49{col 46}{space 3}0.000{col 54}{space 4} .3881808{col 67}{space 3} .4521096
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).8) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Private companies") ///
>         xtitle("") xlabel(0 `""Primary""school""' 1 `""Middle""school""' 2 `""High""school""' 3 `""College""diploma""' 4 `""Bachlor's""degree""' 5 `""Master's""or higher""', labsize(vsmall)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETeducationt5, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:education t5}{p_end}
{res}{txt}
{com}. grc1leg HETeducationt2 HETeducationt3 HETeducationt4 HETeducationt5, ///
>                 graphregion(color(white)) ///
>                 rows(2) ///
>                 name(HETeducation, replace)
{res}{txt}
{com}. restore
{txt}
{com}. 
. 
. 
. *************************************************************
. *** Figure 5: Primed Treatment Effects Conditional on Income
. *************************************************************
. preserve
{txt}
{com}. keep if order==1
{txt}(2,494 observations deleted)

{com}. reg o1_std i.t2##c.income female age education govt_emp income islam r2-r8 if treatment==1 | treatment==2, robust
{txt}{p 0 6 2}note: {bf:income} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       752
                                                {txt}F(14, 737)        =  {res}     5.80
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0738
                                                {txt}Root MSE          =    {res} .38962

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t2 {c |}{col 14}{res}{space 2}-.0182612{col 26}{space 2} .0823123{col 37}{space 1}   -0.22{col 46}{space 3}0.824{col 54}{space 4}-.1798557{col 67}{space 3} .1433332
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0021585{col 26}{space 2} .0074916{col 37}{space 1}    0.29{col 46}{space 3}0.773{col 54}{space 4}-.0125489{col 67}{space 3}  .016866
{txt}{space 12} {c |}
{space 1}t2#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0085466{col 26}{space 2} .0104008{col 37}{space 1}   -0.82{col 46}{space 3}0.411{col 54}{space 4}-.0289654{col 67}{space 3} .0118722
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2}  .036166{col 26}{space 2} .0302405{col 37}{space 1}    1.20{col 46}{space 3}0.232{col 54}{space 4}-.0232017{col 67}{space 3} .0955337
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0030675{col 26}{space 2}  .001267{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.0055547{col 67}{space 3}-.0005802
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0227651{col 26}{space 2}  .013928{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.0501085{col 67}{space 3} .0045782
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0103374{col 26}{space 2} .0395382{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.0672834{col 67}{space 3} .0879583
{txt}{space 6}income {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}islam {c |}{col 14}{res}{space 2} .2597818{col 26}{space 2} .0410594{col 37}{space 1}    6.33{col 46}{space 3}0.000{col 54}{space 4} .1791745{col 67}{space 3} .3403892
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0014721{col 26}{space 2} .0626583{col 37}{space 1}   -0.02{col 46}{space 3}0.981{col 54}{space 4}-.1244821{col 67}{space 3} .1215379
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .1527716{col 26}{space 2} .0723992{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0106384{col 67}{space 3} .2949049
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0867597{col 26}{space 2}   .06679{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0443617{col 67}{space 3} .2178811
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1262041{col 26}{space 2} .0810017{col 37}{space 1}    1.56{col 46}{space 3}0.120{col 54}{space 4}-.0328175{col 67}{space 3} .2852256
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0685449{col 26}{space 2} .0590199{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.0473223{col 67}{space 3} .1844122
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0724746{col 26}{space 2} .0675633{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4}-.0601649{col 67}{space 3} .2051141
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2}   .30495{col 26}{space 2} .1096833{col 37}{space 1}    2.78{col 46}{space 3}0.006{col 54}{space 4} .0896211{col 67}{space 3}  .520279
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t2, at(income=(0(1)10)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:752}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 6:income} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 6:income} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 6:income} = {res:{ralign 2:2}}
{lalign 8:4._at: }{space 0}{lalign 6:income} = {res:{ralign 2:3}}
{lalign 8:5._at: }{space 0}{lalign 6:income} = {res:{ralign 2:4}}
{lalign 8:6._at: }{space 0}{lalign 6:income} = {res:{ralign 2:5}}
{lalign 8:7._at: }{space 0}{lalign 6:income} = {res:{ralign 2:6}}
{lalign 8:8._at: }{space 0}{lalign 6:income} = {res:{ralign 2:7}}
{lalign 8:9._at: }{space 0}{lalign 6:income} = {res:{ralign 2:8}}
{lalign 8:10._at: }{space 0}{lalign 6:income} = {res:{ralign 2:9}}
{lalign 8:11._at: }{space 0}{lalign 6:income} = {res:{ralign 2:10}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t2 {c |}
{space 7} 1 0  {c |}{col 14}{res}{space 2} .4165001{col 26}{space 2} .0578952{col 37}{space 1}    7.19{col 46}{space 3}0.000{col 54}{space 4}  .335071{col 67}{space 3} .4979292
{txt}{space 7} 1 1  {c |}{col 14}{res}{space 2} .3982389{col 26}{space 2} .0638475{col 37}{space 1}    6.24{col 46}{space 3}0.000{col 54}{space 4}  .308438{col 67}{space 3} .4880397
{txt}{space 7} 2 0  {c |}{col 14}{res}{space 2} .4186587{col 26}{space 2} .0509233{col 37}{space 1}    8.22{col 46}{space 3}0.000{col 54}{space 4} .3470355{col 67}{space 3} .4902819
{txt}{space 7} 2 1  {c |}{col 14}{res}{space 2} .3918508{col 26}{space 2}  .056324{col 37}{space 1}    6.96{col 46}{space 3}0.000{col 54}{space 4} .3126317{col 67}{space 3} .4710699
{txt}{space 7} 3 0  {c |}{col 14}{res}{space 2} .4208172{col 26}{space 2} .0441221{col 37}{space 1}    9.54{col 46}{space 3}0.000{col 54}{space 4} .3587598{col 67}{space 3} .4828746
{txt}{space 7} 3 1  {c |}{col 14}{res}{space 2} .3854627{col 26}{space 2} .0489524{col 37}{space 1}    7.87{col 46}{space 3}0.000{col 54}{space 4} .3166117{col 67}{space 3} .4543138
{txt}{space 7} 4 0  {c |}{col 14}{res}{space 2} .4229757{col 26}{space 2} .0375844{col 37}{space 1}   11.25{col 46}{space 3}0.000{col 54}{space 4} .3701136{col 67}{space 3} .4758379
{txt}{space 7} 4 1  {c |}{col 14}{res}{space 2} .3790746{col 26}{space 2} .0418131{col 37}{space 1}    9.07{col 46}{space 3}0.000{col 54}{space 4}  .320265{col 67}{space 3} .4378843
{txt}{space 7} 5 0  {c |}{col 14}{res}{space 2} .4251343{col 26}{space 2} .0314748{col 37}{space 1}   13.51{col 46}{space 3}0.000{col 54}{space 4} .3808652{col 67}{space 3} .4694033
{txt}{space 7} 5 1  {c |}{col 14}{res}{space 2} .3726866{col 26}{space 2} .0350484{col 37}{space 1}   10.63{col 46}{space 3}0.000{col 54}{space 4} .3233914{col 67}{space 3} .4219818
{txt}{space 7} 6 0  {c |}{col 14}{res}{space 2} .4272928{col 26}{space 2} .0260958{col 37}{space 1}   16.37{col 46}{space 3}0.000{col 54}{space 4} .3905894{col 67}{space 3} .4639963
{txt}{space 7} 6 1  {c |}{col 14}{res}{space 2} .3662985{col 26}{space 2} .0289223{col 37}{space 1}   12.66{col 46}{space 3}0.000{col 54}{space 4} .3256196{col 67}{space 3} .4069774
{txt}{space 7} 7 0  {c |}{col 14}{res}{space 2} .4294513{col 26}{space 2}   .02199{col 37}{space 1}   19.53{col 46}{space 3}0.000{col 54}{space 4} .3985226{col 67}{space 3} .4603801
{txt}{space 7} 7 1  {c |}{col 14}{res}{space 2} .3599104{col 26}{space 2} .0239304{col 37}{space 1}   15.04{col 46}{space 3}0.000{col 54}{space 4} .3262525{col 67}{space 3} .3935683
{txt}{space 7} 8 0  {c |}{col 14}{res}{space 2} .4316099{col 26}{space 2} .0199596{col 37}{space 1}   21.62{col 46}{space 3}0.000{col 54}{space 4} .4035369{col 67}{space 3} .4596828
{txt}{space 7} 8 1  {c |}{col 14}{res}{space 2} .3535223{col 26}{space 2} .0209018{col 37}{space 1}   16.91{col 46}{space 3}0.000{col 54}{space 4} .3241242{col 67}{space 3} .3829205
{txt}{space 7} 9 0  {c |}{col 14}{res}{space 2} .4337684{col 26}{space 2} .0206266{col 37}{space 1}   21.03{col 46}{space 3}0.000{col 54}{space 4} .4047573{col 67}{space 3} .4627795
{txt}{space 7} 9 1  {c |}{col 14}{res}{space 2} .3471343{col 26}{space 2} .0207161{col 37}{space 1}   16.76{col 46}{space 3}0.000{col 54}{space 4} .3179972{col 67}{space 3} .3762713
{txt}{space 7}10 0  {c |}{col 14}{res}{space 2}  .435927{col 26}{space 2}  .023765{col 37}{space 1}   18.34{col 46}{space 3}0.000{col 54}{space 4} .4025017{col 67}{space 3} .4693522
{txt}{space 7}10 1  {c |}{col 14}{res}{space 2} .3407462{col 26}{space 2} .0234412{col 37}{space 1}   14.54{col 46}{space 3}0.000{col 54}{space 4} .3077764{col 67}{space 3}  .373716
{txt}{space 7}11 0  {c |}{col 14}{res}{space 2} .4380855{col 26}{space 2} .0285718{col 37}{space 1}   15.33{col 46}{space 3}0.000{col 54}{space 4} .3978996{col 67}{space 3} .4782714
{txt}{space 7}11 1  {c |}{col 14}{res}{space 2} .3343581{col 26}{space 2} .0282467{col 37}{space 1}   11.84{col 46}{space 3}0.000{col 54}{space 4} .2946294{col 67}{space 3} .3740868
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).6) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Force majeure") ///
>         xtitle("") xlabel(0 `""Less than""2,500 TL""' 1 `""2,500-""4,999 TL""' 2 `""5,000-""7,499 TL""' 3 `""7,500-""9,999 TL""' 4 `""10,000-""12,499 TL""' 5 `""12,500-""14,999 TL""' 6 `""15,000-""17,499 TL""' 7 `""17,500-""19,999 TL""' 8 `""20,000-""22,499 TL""' 9 `""22,500-""24,999 TL""' 10 `""25,000 TL""or more""', labsize(tiny)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETincomet2, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:income t2}{p_end}
{res}{txt}
{com}. reg o1_std i.t3##c.income female age education govt_emp income islam r2-r8 if treatment==1 | treatment==3, robust
{txt}{p 0 6 2}note: {bf:income} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       768
                                                {txt}F(14, 753)        =  {res}     5.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0729
                                                {txt}Root MSE          =    {res} .38998

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t3 {c |}{col 14}{res}{space 2} .0031552{col 26}{space 2} .0840547{col 37}{space 1}    0.04{col 46}{space 3}0.970{col 54}{space 4}-.1618542{col 67}{space 3} .1681646
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0029122{col 26}{space 2} .0075463{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4} -.011902{col 67}{space 3} .0177264
{txt}{space 12} {c |}
{space 1}t3#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0013753{col 26}{space 2}  .010455{col 37}{space 1}   -0.13{col 46}{space 3}0.895{col 54}{space 4}-.0218997{col 67}{space 3} .0191491
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0145867{col 26}{space 2} .0296779{col 37}{space 1}    0.49{col 46}{space 3}0.623{col 54}{space 4}-.0436745{col 67}{space 3}  .072848
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0032843{col 26}{space 2} .0013026{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.0058416{col 67}{space 3}-.0007271
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0309646{col 26}{space 2} .0140062{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4}-.0584604{col 67}{space 3}-.0034688
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0065216{col 26}{space 2} .0416856{col 37}{space 1}   -0.16{col 46}{space 3}0.876{col 54}{space 4}-.0883555{col 67}{space 3} .0753123
{txt}{space 6}income {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}islam {c |}{col 14}{res}{space 2} .2855626{col 26}{space 2} .0417149{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .2036713{col 67}{space 3} .3674539
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0404968{col 26}{space 2} .0642358{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4}-.1665993{col 67}{space 3} .0856056
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}  .077236{col 26}{space 2} .0718482{col 37}{space 1}    1.07{col 46}{space 3}0.283{col 54}{space 4}-.0638107{col 67}{space 3} .2182826
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1044748{col 26}{space 2} .0666328{col 37}{space 1}    1.57{col 46}{space 3}0.117{col 54}{space 4}-.0263334{col 67}{space 3}  .235283
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0658341{col 26}{space 2} .0760017{col 37}{space 1}    0.87{col 46}{space 3}0.387{col 54}{space 4}-.0833662{col 67}{space 3} .2150345
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0517405{col 26}{space 2} .0593071{col 37}{space 1}    0.87{col 46}{space 3}0.383{col 54}{space 4}-.0646865{col 67}{space 3} .1681675
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}  .027702{col 26}{space 2} .0673344{col 37}{space 1}    0.41{col 46}{space 3}0.681{col 54}{space 4}-.1044835{col 67}{space 3} .1598875
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2}  .358834{col 26}{space 2} .1092857{col 37}{space 1}    3.28{col 46}{space 3}0.001{col 54}{space 4} .1442932{col 67}{space 3} .5733748
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t3, at(income=(0(1)10)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:768}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 6:income} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 6:income} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 6:income} = {res:{ralign 2:2}}
{lalign 8:4._at: }{space 0}{lalign 6:income} = {res:{ralign 2:3}}
{lalign 8:5._at: }{space 0}{lalign 6:income} = {res:{ralign 2:4}}
{lalign 8:6._at: }{space 0}{lalign 6:income} = {res:{ralign 2:5}}
{lalign 8:7._at: }{space 0}{lalign 6:income} = {res:{ralign 2:6}}
{lalign 8:8._at: }{space 0}{lalign 6:income} = {res:{ralign 2:7}}
{lalign 8:9._at: }{space 0}{lalign 6:income} = {res:{ralign 2:8}}
{lalign 8:10._at: }{space 0}{lalign 6:income} = {res:{ralign 2:9}}
{lalign 8:11._at: }{space 0}{lalign 6:income} = {res:{ralign 2:10}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t3 {c |}
{space 7} 1 0  {c |}{col 14}{res}{space 2} .4073582{col 26}{space 2} .0587647{col 37}{space 1}    6.93{col 46}{space 3}0.000{col 54}{space 4}  .324708{col 67}{space 3} .4900084
{txt}{space 7} 1 1  {c |}{col 14}{res}{space 2} .4105134{col 26}{space 2} .0656295{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} .3182081{col 67}{space 3} .5028187
{txt}{space 7} 2 0  {c |}{col 14}{res}{space 2} .4102705{col 26}{space 2} .0517263{col 37}{space 1}    7.93{col 46}{space 3}0.000{col 54}{space 4} .3375195{col 67}{space 3} .4830215
{txt}{space 7} 2 1  {c |}{col 14}{res}{space 2} .4120503{col 26}{space 2} .0579985{col 37}{space 1}    7.10{col 46}{space 3}0.000{col 54}{space 4} .3304778{col 67}{space 3} .4936229
{txt}{space 7} 3 0  {c |}{col 14}{res}{space 2} .4131827{col 26}{space 2} .0448534{col 37}{space 1}    9.21{col 46}{space 3}0.000{col 54}{space 4} .3500982{col 67}{space 3} .4762672
{txt}{space 7} 3 1  {c |}{col 14}{res}{space 2} .4135872{col 26}{space 2} .0505041{col 37}{space 1}    8.19{col 46}{space 3}0.000{col 54}{space 4} .3425553{col 67}{space 3} .4846192
{txt}{space 7} 4 0  {c |}{col 14}{res}{space 2}  .416095{col 26}{space 2} .0382352{col 37}{space 1}   10.88{col 46}{space 3}0.000{col 54}{space 4} .3623186{col 67}{space 3} .4698713
{txt}{space 7} 4 1  {c |}{col 14}{res}{space 2} .4151241{col 26}{space 2} .0432173{col 37}{space 1}    9.61{col 46}{space 3}0.000{col 54}{space 4} .3543407{col 67}{space 3} .4759076
{txt}{space 7} 5 0  {c |}{col 14}{res}{space 2} .4190072{col 26}{space 2} .0320302{col 37}{space 1}   13.08{col 46}{space 3}0.000{col 54}{space 4}  .373958{col 67}{space 3} .4640564
{txt}{space 7} 5 1  {c |}{col 14}{res}{space 2}  .416661{col 26}{space 2} .0362637{col 37}{space 1}   11.49{col 46}{space 3}0.000{col 54}{space 4} .3656576{col 67}{space 3} .4676644
{txt}{space 7} 6 0  {c |}{col 14}{res}{space 2} .4219194{col 26}{space 2} .0265297{col 37}{space 1}   15.90{col 46}{space 3}0.000{col 54}{space 4} .3846065{col 67}{space 3} .4592324
{txt}{space 7} 6 1  {c |}{col 14}{res}{space 2} .4181979{col 26}{space 2} .0298766{col 37}{space 1}   14.00{col 46}{space 3}0.000{col 54}{space 4} .3761777{col 67}{space 3} .4602182
{txt}{space 7} 7 0  {c |}{col 14}{res}{space 2} .4248317{col 26}{space 2} .0222622{col 37}{space 1}   19.08{col 46}{space 3}0.000{col 54}{space 4} .3935208{col 67}{space 3} .4561426
{txt}{space 7} 7 1  {c |}{col 14}{res}{space 2} .4197348{col 26}{space 2} .0245033{col 37}{space 1}   17.13{col 46}{space 3}0.000{col 54}{space 4} .3852719{col 67}{space 3} .4541977
{txt}{space 7} 8 0  {c |}{col 14}{res}{space 2} .4277439{col 26}{space 2}  .020032{col 37}{space 1}   21.35{col 46}{space 3}0.000{col 54}{space 4} .3995697{col 67}{space 3} .4559181
{txt}{space 7} 8 1  {c |}{col 14}{res}{space 2} .4212717{col 26}{space 2} .0209392{col 37}{space 1}   20.12{col 46}{space 3}0.000{col 54}{space 4} .3918215{col 67}{space 3} .4507219
{txt}{space 7} 9 0  {c |}{col 14}{res}{space 2} .4306562{col 26}{space 2} .0205146{col 37}{space 1}   20.99{col 46}{space 3}0.000{col 54}{space 4} .4018032{col 67}{space 3} .4595091
{txt}{space 7} 9 1  {c |}{col 14}{res}{space 2} .4228086{col 26}{space 2} .0201676{col 37}{space 1}   20.96{col 46}{space 3}0.000{col 54}{space 4} .3944436{col 67}{space 3} .4511736
{txt}{space 7}10 0  {c |}{col 14}{res}{space 2} .4335684{col 26}{space 2} .0235437{col 37}{space 1}   18.42{col 46}{space 3}0.000{col 54}{space 4} .4004551{col 67}{space 3} .4666817
{txt}{space 7}10 1  {c |}{col 14}{res}{space 2} .4243455{col 26}{space 2} .0224779{col 37}{space 1}   18.88{col 46}{space 3}0.000{col 54}{space 4} .3927312{col 67}{space 3} .4559598
{txt}{space 7}11 0  {c |}{col 14}{res}{space 2} .4364807{col 26}{space 2} .0283136{col 37}{space 1}   15.42{col 46}{space 3}0.000{col 54}{space 4} .3966588{col 67}{space 3} .4763026
{txt}{space 7}11 1  {c |}{col 14}{res}{space 2} .4258824{col 26}{space 2} .0270928{col 37}{space 1}   15.72{col 46}{space 3}0.000{col 54}{space 4} .3877774{col 67}{space 3} .4639874
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).6) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Minister") ///
>         xtitle("") xlabel(0 `""Less than""2,500 TL""' 1 `""2,500-""4,999 TL""' 2 `""5,000-""7,499 TL""' 3 `""7,500-""9,999 TL""' 4 `""10,000-""12,499 TL""' 5 `""12,500-""14,999 TL""' 6 `""15,000-""17,499 TL""' 7 `""17,500-""19,999 TL""' 8 `""20,000-""22,499 TL""' 9 `""22,500-""24,999 TL""' 10 `""25,000 TL""or more""', labsize(tiny)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETincomet3, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:income t3}{p_end}
{res}{txt}
{com}. reg o1_std i.t4##c.income female age education govt_emp income islam r2-r8 if treatment==1 | treatment==4, robust
{txt}{p 0 6 2}note: {bf:income} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       762
                                                {txt}F(14, 747)        =  {res}     8.89
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1090
                                                {txt}Root MSE          =    {res} .38358

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t4 {c |}{col 14}{res}{space 2} .0312531{col 26}{space 2} .0781305{col 37}{space 1}    0.40{col 46}{space 3}0.689{col 54}{space 4}-.1221284{col 67}{space 3} .1846345
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0056914{col 26}{space 2} .0074545{col 37}{space 1}    0.76{col 46}{space 3}0.445{col 54}{space 4} -.008943{col 67}{space 3} .0203257
{txt}{space 12} {c |}
{space 1}t4#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0121148{col 26}{space 2}  .009946{col 37}{space 1}   -1.22{col 46}{space 3}0.224{col 54}{space 4}-.0316402{col 67}{space 3} .0074106
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0499015{col 26}{space 2} .0291797{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0073825{col 67}{space 3} .1071855
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0044502{col 26}{space 2} .0012422{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0068889{col 67}{space 3}-.0020116
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0407412{col 26}{space 2} .0136425{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4}-.0675235{col 67}{space 3}-.0139589
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0296811{col 26}{space 2} .0394932{col 37}{space 1}   -0.75{col 46}{space 3}0.453{col 54}{space 4} -.107212{col 67}{space 3} .0478497
{txt}{space 6}income {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}islam {c |}{col 14}{res}{space 2} .3035766{col 26}{space 2} .0390956{col 37}{space 1}    7.76{col 46}{space 3}0.000{col 54}{space 4} .2268263{col 67}{space 3} .3803269
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0064108{col 26}{space 2} .0677247{col 37}{space 1}    0.09{col 46}{space 3}0.925{col 54}{space 4}-.1265426{col 67}{space 3} .1393641
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .1425204{col 26}{space 2} .0737534{col 37}{space 1}    1.93{col 46}{space 3}0.054{col 54}{space 4}-.0022683{col 67}{space 3}  .287309
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1198537{col 26}{space 2} .0696096{col 37}{space 1}    1.72{col 46}{space 3}0.086{col 54}{space 4}   -.0168{col 67}{space 3} .2565074
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .2078586{col 26}{space 2} .0738595{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0628616{col 67}{space 3} .3528556
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0712543{col 26}{space 2} .0615361{col 37}{space 1}    1.16{col 46}{space 3}0.247{col 54}{space 4}-.0495499{col 67}{space 3} .1920586
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0884473{col 26}{space 2} .0699267{col 37}{space 1}    1.26{col 46}{space 3}0.206{col 54}{space 4} -.048829{col 67}{space 3} .2257236
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .3571372{col 26}{space 2} .1111764{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .1388819{col 67}{space 3} .5753925
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t4, at(income=(0(1)10)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:762}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 6:income} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 6:income} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 6:income} = {res:{ralign 2:2}}
{lalign 8:4._at: }{space 0}{lalign 6:income} = {res:{ralign 2:3}}
{lalign 8:5._at: }{space 0}{lalign 6:income} = {res:{ralign 2:4}}
{lalign 8:6._at: }{space 0}{lalign 6:income} = {res:{ralign 2:5}}
{lalign 8:7._at: }{space 0}{lalign 6:income} = {res:{ralign 2:6}}
{lalign 8:8._at: }{space 0}{lalign 6:income} = {res:{ralign 2:7}}
{lalign 8:9._at: }{space 0}{lalign 6:income} = {res:{ralign 2:8}}
{lalign 8:10._at: }{space 0}{lalign 6:income} = {res:{ralign 2:9}}
{lalign 8:11._at: }{space 0}{lalign 6:income} = {res:{ralign 2:10}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t4 {c |}
{space 7} 1 0  {c |}{col 14}{res}{space 2} .3892854{col 26}{space 2} .0576562{col 37}{space 1}    6.75{col 46}{space 3}0.000{col 54}{space 4} .3081937{col 67}{space 3} .4703772
{txt}{space 7} 1 1  {c |}{col 14}{res}{space 2} .4205385{col 26}{space 2} .0589831{col 37}{space 1}    7.13{col 46}{space 3}0.000{col 54}{space 4} .3375804{col 67}{space 3} .5034966
{txt}{space 7} 2 0  {c |}{col 14}{res}{space 2} .3949768{col 26}{space 2} .0507208{col 37}{space 1}    7.79{col 46}{space 3}0.000{col 54}{space 4} .3236395{col 67}{space 3} .4663141
{txt}{space 7} 2 1  {c |}{col 14}{res}{space 2} .4141151{col 26}{space 2} .0519465{col 37}{space 1}    7.97{col 46}{space 3}0.000{col 54}{space 4} .3410538{col 67}{space 3} .4871764
{txt}{space 7} 3 0  {c |}{col 14}{res}{space 2} .4006682{col 26}{space 2} .0439557{col 37}{space 1}    9.12{col 46}{space 3}0.000{col 54}{space 4} .3388458{col 67}{space 3} .4624906
{txt}{space 7} 3 1  {c |}{col 14}{res}{space 2} .4076916{col 26}{space 2} .0450664{col 37}{space 1}    9.05{col 46}{space 3}0.000{col 54}{space 4}  .344307{col 67}{space 3} .4710762
{txt}{space 7} 4 0  {c |}{col 14}{res}{space 2} .4063596{col 26}{space 2} .0374533{col 37}{space 1}   10.85{col 46}{space 3}0.000{col 54}{space 4} .3536826{col 67}{space 3} .4590365
{txt}{space 7} 4 1  {c |}{col 14}{res}{space 2} .4012682{col 26}{space 2} .0384268{col 37}{space 1}   10.44{col 46}{space 3}0.000{col 54}{space 4} .3472219{col 67}{space 3} .4553144
{txt}{space 7} 5 0  {c |}{col 14}{res}{space 2}  .412051{col 26}{space 2} .0313773{col 37}{space 1}   13.13{col 46}{space 3}0.000{col 54}{space 4} .3679197{col 67}{space 3} .4561822
{txt}{space 7} 5 1  {c |}{col 14}{res}{space 2} .3948447{col 26}{space 2} .0321772{col 37}{space 1}   12.27{col 46}{space 3}0.000{col 54}{space 4} .3495885{col 67}{space 3}  .440101
{txt}{space 7} 6 0  {c |}{col 14}{res}{space 2} .4177423{col 26}{space 2}  .026028{col 37}{space 1}   16.05{col 46}{space 3}0.000{col 54}{space 4} .3811347{col 67}{space 3}   .45435
{txt}{space 7} 6 1  {c |}{col 14}{res}{space 2} .3884213{col 26}{space 2} .0265937{col 37}{space 1}   14.61{col 46}{space 3}0.000{col 54}{space 4}  .351018{col 67}{space 3} .4258246
{txt}{space 7} 7 0  {c |}{col 14}{res}{space 2} .4234337{col 26}{space 2} .0219437{col 37}{space 1}   19.30{col 46}{space 3}0.000{col 54}{space 4} .3925706{col 67}{space 3} .4542969
{txt}{space 7} 7 1  {c |}{col 14}{res}{space 2} .3819978{col 26}{space 2} .0221853{col 37}{space 1}   17.22{col 46}{space 3}0.000{col 54}{space 4} .3507949{col 67}{space 3} .4132008
{txt}{space 7} 8 0  {c |}{col 14}{res}{space 2} .4291251{col 26}{space 2} .0199182{col 37}{space 1}   21.54{col 46}{space 3}0.000{col 54}{space 4} .4011108{col 67}{space 3} .4571394
{txt}{space 7} 8 1  {c |}{col 14}{res}{space 2} .3755744{col 26}{space 2} .0197548{col 37}{space 1}   19.01{col 46}{space 3}0.000{col 54}{space 4} .3477898{col 67}{space 3}  .403359
{txt}{space 7} 9 0  {c |}{col 14}{res}{space 2} .4348165{col 26}{space 2} .0205689{col 37}{space 1}   21.14{col 46}{space 3}0.000{col 54}{space 4} .4058869{col 67}{space 3} .4637461
{txt}{space 7} 9 1  {c |}{col 14}{res}{space 2} .3691509{col 26}{space 2} .0200356{col 37}{space 1}   18.42{col 46}{space 3}0.000{col 54}{space 4} .3409714{col 67}{space 3} .3973305
{txt}{space 7}10 0  {c |}{col 14}{res}{space 2} .4405079{col 26}{space 2} .0236764{col 37}{space 1}   18.61{col 46}{space 3}0.000{col 54}{space 4} .4072078{col 67}{space 3}  .473808
{txt}{space 7}10 1  {c |}{col 14}{res}{space 2} .3627275{col 26}{space 2} .0229282{col 37}{space 1}   15.82{col 46}{space 3}0.000{col 54}{space 4} .3304796{col 67}{space 3} .3949754
{txt}{space 7}11 0  {c |}{col 14}{res}{space 2} .4461993{col 26}{space 2} .0284464{col 37}{space 1}   15.69{col 46}{space 3}0.000{col 54}{space 4} .4061902{col 67}{space 3} .4862084
{txt}{space 7}11 1  {c |}{col 14}{res}{space 2} .3563041{col 26}{space 2} .0276241{col 37}{space 1}   12.90{col 46}{space 3}0.000{col 54}{space 4} .3174516{col 67}{space 3} .3951565
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).6) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Opposition") ///
>         xtitle("") xlabel(0 `""Less than""2,500 TL""' 1 `""2,500-""4,999 TL""' 2 `""5,000-""7,499 TL""' 3 `""7,500-""9,999 TL""' 4 `""10,000-""12,499 TL""' 5 `""12,500-""14,999 TL""' 6 `""15,000-""17,499 TL""' 7 `""17,500-""19,999 TL""' 8 `""20,000-""22,499 TL""' 9 `""22,500-""24,999 TL""' 10 `""25,000 TL""or more""', labsize(tiny)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETincomet4, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:income t4}{p_end}
{res}{txt}
{com}. reg o1_std i.t5##c.income female age education govt_emp income islam r2-r8 if treatment==1 | treatment==5, robust
{txt}{p 0 6 2}note: {bf:income} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       756
                                                {txt}F(14, 741)        =  {res}     6.60
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0831
                                                {txt}Root MSE          =    {res} .39165

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t5 {c |}{col 14}{res}{space 2} .1072306{col 26}{space 2} .0825831{col 37}{space 1}    1.30{col 46}{space 3}0.195{col 54}{space 4}-.0548941{col 67}{space 3} .2693554
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0019886{col 26}{space 2} .0076136{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.0129582{col 67}{space 3} .0169353
{txt}{space 12} {c |}
{space 1}t5#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0140023{col 26}{space 2} .0103459{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-.0343132{col 67}{space 3} .0063085
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0346639{col 26}{space 2} .0297861{col 37}{space 1}    1.16{col 46}{space 3}0.245{col 54}{space 4}-.0238113{col 67}{space 3} .0931391
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0027059{col 26}{space 2} .0012876{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.0052337{col 67}{space 3}-.0001782
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0191419{col 26}{space 2} .0139622{col 37}{space 1}   -1.37{col 46}{space 3}0.171{col 54}{space 4} -.046552{col 67}{space 3} .0082683
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0224682{col 26}{space 2} .0410087{col 37}{space 1}   -0.55{col 46}{space 3}0.584{col 54}{space 4}-.1029753{col 67}{space 3} .0580388
{txt}{space 6}income {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}islam {c |}{col 14}{res}{space 2} .3022256{col 26}{space 2} .0436189{col 37}{space 1}    6.93{col 46}{space 3}0.000{col 54}{space 4} .2165943{col 67}{space 3}  .387857
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0412157{col 26}{space 2} .0650181{col 37}{space 1}   -0.63{col 46}{space 3}0.526{col 54}{space 4}-.1688574{col 67}{space 3}  .086426
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0799384{col 26}{space 2} .0732448{col 37}{space 1}    1.09{col 46}{space 3}0.275{col 54}{space 4}-.0638537{col 67}{space 3} .2237304
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1314591{col 26}{space 2} .0698825{col 37}{space 1}    1.88{col 46}{space 3}0.060{col 54}{space 4}-.0057321{col 67}{space 3} .2686503
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1985862{col 26}{space 2}   .07976{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} .0420037{col 67}{space 3} .3551687
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0601414{col 26}{space 2} .0610334{col 37}{space 1}    0.99{col 46}{space 3}0.325{col 54}{space 4}-.0596776{col 67}{space 3} .1799605
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0615843{col 26}{space 2} .0682163{col 37}{space 1}    0.90{col 46}{space 3}0.367{col 54}{space 4} -.072336{col 67}{space 3} .1955046
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2525035{col 26}{space 2} .1116536{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .0333085{col 67}{space 3} .4716986
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins t5, at(income=(0(1)10)) level(84)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:756}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 6:income} = {res:{ralign 2:0}}
{lalign 8:2._at: }{space 0}{lalign 6:income} = {res:{ralign 2:1}}
{lalign 8:3._at: }{space 0}{lalign 6:income} = {res:{ralign 2:2}}
{lalign 8:4._at: }{space 0}{lalign 6:income} = {res:{ralign 2:3}}
{lalign 8:5._at: }{space 0}{lalign 6:income} = {res:{ralign 2:4}}
{lalign 8:6._at: }{space 0}{lalign 6:income} = {res:{ralign 2:5}}
{lalign 8:7._at: }{space 0}{lalign 6:income} = {res:{ralign 2:6}}
{lalign 8:8._at: }{space 0}{lalign 6:income} = {res:{ralign 2:7}}
{lalign 8:9._at: }{space 0}{lalign 6:income} = {res:{ralign 2:8}}
{lalign 8:10._at: }{space 0}{lalign 6:income} = {res:{ralign 2:9}}
{lalign 8:11._at: }{space 0}{lalign 6:income} = {res:{ralign 2:10}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [84% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}_at#t5 {c |}
{space 7} 1 0  {c |}{col 14}{res}{space 2} .4170242{col 26}{space 2} .0592166{col 37}{space 1}    7.04{col 46}{space 3}0.000{col 54}{space 4}  .333737{col 67}{space 3} .5003113
{txt}{space 7} 1 1  {c |}{col 14}{res}{space 2} .5242548{col 26}{space 2} .0629971{col 37}{space 1}    8.32{col 46}{space 3}0.000{col 54}{space 4} .4356504{col 67}{space 3} .6128592
{txt}{space 7} 2 0  {c |}{col 14}{res}{space 2} .4190127{col 26}{space 2} .0521066{col 37}{space 1}    8.04{col 46}{space 3}0.000{col 54}{space 4} .3457256{col 67}{space 3} .4922998
{txt}{space 7} 2 1  {c |}{col 14}{res}{space 2}  .512241{col 26}{space 2} .0556655{col 37}{space 1}    9.20{col 46}{space 3}0.000{col 54}{space 4} .4339484{col 67}{space 3} .5905337
{txt}{space 7} 3 0  {c |}{col 14}{res}{space 2} .4210013{col 26}{space 2} .0451611{col 37}{space 1}    9.32{col 46}{space 3}0.000{col 54}{space 4}  .357483{col 67}{space 3} .4845196
{txt}{space 7} 3 1  {c |}{col 14}{res}{space 2} .5002273{col 26}{space 2} .0484845{col 37}{space 1}   10.32{col 46}{space 3}0.000{col 54}{space 4} .4320347{col 67}{space 3} .5684199
{txt}{space 7} 4 0  {c |}{col 14}{res}{space 2} .4229898{col 26}{space 2} .0384693{col 37}{space 1}   11.00{col 46}{space 3}0.000{col 54}{space 4} .3688835{col 67}{space 3} .4770962
{txt}{space 7} 4 1  {c |}{col 14}{res}{space 2} .4882135{col 26}{space 2} .0415321{col 37}{space 1}   11.76{col 46}{space 3}0.000{col 54}{space 4} .4297993{col 67}{space 3} .5466277
{txt}{space 7} 5 0  {c |}{col 14}{res}{space 2} .4249784{col 26}{space 2} .0321897{col 37}{space 1}   13.20{col 46}{space 3}0.000{col 54}{space 4} .3797042{col 67}{space 3} .4702526
{txt}{space 7} 5 1  {c |}{col 14}{res}{space 2} .4761998{col 26}{space 2} .0349452{col 37}{space 1}   13.63{col 46}{space 3}0.000{col 54}{space 4}   .42705{col 67}{space 3} .5253496
{txt}{space 7} 6 0  {c |}{col 14}{res}{space 2}  .426967{col 26}{space 2} .0266158{col 37}{space 1}   16.04{col 46}{space 3}0.000{col 54}{space 4} .3895323{col 67}{space 3} .4644016
{txt}{space 7} 6 1  {c |}{col 14}{res}{space 2}  .464186{col 26}{space 2}  .028974{col 37}{space 1}   16.02{col 46}{space 3}0.000{col 54}{space 4} .4234346{col 67}{space 3} .5049375
{txt}{space 7} 7 0  {c |}{col 14}{res}{space 2} .4289555{col 26}{space 2} .0222836{col 37}{space 1}   19.25{col 46}{space 3}0.000{col 54}{space 4}  .397614{col 67}{space 3}  .460297
{txt}{space 7} 7 1  {c |}{col 14}{res}{space 2} .4521723{col 26}{space 2} .0240811{col 37}{space 1}   18.78{col 46}{space 3}0.000{col 54}{space 4} .4183026{col 67}{space 3} .4860419
{txt}{space 7} 8 0  {c |}{col 14}{res}{space 2} .4309441{col 26}{space 2} .0200162{col 37}{space 1}   21.53{col 46}{space 3}0.000{col 54}{space 4} .4027916{col 67}{space 3} .4590965
{txt}{space 7} 8 1  {c |}{col 14}{res}{space 2} .4401585{col 26}{space 2} .0210329{col 37}{space 1}   20.93{col 46}{space 3}0.000{col 54}{space 4} .4105761{col 67}{space 3} .4697409
{txt}{space 7} 9 0  {c |}{col 14}{res}{space 2} .4329326{col 26}{space 2} .0205103{col 37}{space 1}   21.11{col 46}{space 3}0.000{col 54}{space 4} .4040852{col 67}{space 3}   .46178
{txt}{space 7} 9 1  {c |}{col 14}{res}{space 2} .4281448{col 26}{space 2} .0206627{col 37}{space 1}   20.72{col 46}{space 3}0.000{col 54}{space 4} .3990831{col 67}{space 3} .4572065
{txt}{space 7}10 0  {c |}{col 14}{res}{space 2} .4349212{col 26}{space 2}  .023593{col 37}{space 1}   18.43{col 46}{space 3}0.000{col 54}{space 4}  .401738{col 67}{space 3} .4681044
{txt}{space 7}10 1  {c |}{col 14}{res}{space 2}  .416131{col 26}{space 2} .0230995{col 37}{space 1}   18.01{col 46}{space 3}0.000{col 54}{space 4}  .383642{col 67}{space 3} .4486201
{txt}{space 7}11 0  {c |}{col 14}{res}{space 2} .4369097{col 26}{space 2} .0284345{col 37}{space 1}   15.37{col 46}{space 3}0.000{col 54}{space 4} .3969171{col 67}{space 3} .4769024
{txt}{space 7}11 1  {c |}{col 14}{res}{space 2} .4041173{col 26}{space 2} .0276099{col 37}{space 1}   14.64{col 46}{space 3}0.000{col 54}{space 4} .3652845{col 67}{space 3} .4429501
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, ///
>         scheme(plotplain) ///
>         yscale(range (.2 .6)) ylabel(.2(.1).6) ytitle("Erdoğan approval") ///
>         byopts(title("")) ///
>         ci1opt(color(gray)) ci2opt(color(black)) ///
>         plot1opts(msymbol(oh) mcolor(gray) lcolor(gray)) plot2opts(msymbol(X) mcolor(black) lcolor(black)) ///  
>         title("Treatment: Private companies") ///
>         xtitle("") xlabel(0 `""Less than""2,500 TL""' 1 `""2,500-""4,999 TL""' 2 `""5,000-""7,499 TL""' 3 `""7,500-""9,999 TL""' 4 `""10,000-""12,499 TL""' 5 `""12,500-""14,999 TL""' 6 `""15,000-""17,499 TL""' 7 `""17,500-""19,999 TL""' 8 `""20,000-""22,499 TL""' 9 `""22,500-""24,999 TL""' 10 `""25,000 TL""or more""', labsize(tiny)) ///
>         legend(order(3 "Control" 4 "Treatment") pos(6) rows(1)) ///
>         name(HETincomet5, replace)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:income t5}{p_end}
{res}{txt}
{com}. 
. grc1leg HETincomet2 HETincomet3 HETincomet4 HETincomet5, ///
>                 graphregion(color(white)) ///
>                 rows(2) ///
>                 name(HTEincome, replace)
{res}{txt}
{com}. restore
{txt}
{com}. 
. 
. 
. **********************************************************************
. *** Table 2: Summary of Treatment Effects Conditional on Partisanship
. **********************************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==1, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,585
                                                {txt}F(16, 1568)       =  {res}     2.74
                                                {txt}Prob > F          = {res}    0.0002
                                                {txt}R-squared         = {res}    0.0261
                                                {txt}Root MSE          =    {res} .33731

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0806968{col 26}{space 2} .0275648{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.1347646{col 67}{space 3} -.026629
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0295449{col 26}{space 2} .0261151{col 37}{space 1}   -1.13{col 46}{space 3}0.258{col 54}{space 4}-.0807691{col 67}{space 3} .0216792
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0013844{col 26}{space 2} .0257061{col 37}{space 1}   -0.05{col 46}{space 3}0.957{col 54}{space 4}-.0518063{col 67}{space 3} .0490375
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0020452{col 26}{space 2} .0261566{col 37}{space 1}   -0.08{col 46}{space 3}0.938{col 54}{space 4}-.0533507{col 67}{space 3} .0492603
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0325775{col 26}{space 2} .0182822{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0032826{col 67}{space 3} .0684376
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0010079{col 26}{space 2} .0008491{col 37}{space 1}    1.19{col 46}{space 3}0.235{col 54}{space 4}-.0006576{col 67}{space 3} .0026733
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.013334{col 26}{space 2} .0076022{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}-.0282457{col 67}{space 3} .0015776
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0077341{col 26}{space 2}  .024102{col 37}{space 1}    0.32{col 46}{space 3}0.748{col 54}{space 4}-.0395415{col 67}{space 3} .0550096
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0011883{col 26}{space 2} .0034362{col 37}{space 1}   -0.35{col 46}{space 3}0.730{col 54}{space 4}-.0079284{col 67}{space 3} .0055517
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0815711{col 26}{space 2} .0780264{col 37}{space 1}    1.05{col 46}{space 3}0.296{col 54}{space 4}-.0714759{col 67}{space 3} .2346181
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0947539{col 26}{space 2} .0424574{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-.1780331{col 67}{space 3}-.0114746
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0532765{col 26}{space 2} .0396655{col 37}{space 1}    1.34{col 46}{space 3}0.179{col 54}{space 4}-.0245266{col 67}{space 3} .1310796
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0064118{col 26}{space 2} .0376523{col 37}{space 1}    0.17{col 46}{space 3}0.865{col 54}{space 4}-.0674424{col 67}{space 3} .0802659
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0186887{col 26}{space 2}  .034334{col 37}{space 1}   -0.54{col 46}{space 3}0.586{col 54}{space 4}-.0860341{col 67}{space 3} .0486568
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} -.032506{col 26}{space 2} .0399125{col 37}{space 1}   -0.81{col 46}{space 3}0.416{col 54}{space 4}-.1107934{col 67}{space 3} .0457815
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0190759{col 26}{space 2} .0425049{col 37}{space 1}   -0.45{col 46}{space 3}0.654{col 54}{space 4}-.1024483{col 67}{space 3} .0642964
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6752114{col 26}{space 2}  .094069{col 37}{space 1}    7.18{col 46}{space 3}0.000{col 54}{space 4} .4906971{col 67}{space 3} .8597256
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1585}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Erdoğan"

{txt}added macro:
             e(middle) : "{res:Erdoğan}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,233
                                                {txt}F(16, 1216)       =  {res}     4.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0529
                                                {txt}Root MSE          =    {res} .24009

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0483316{col 26}{space 2} .0202495{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.0880595{col 67}{space 3}-.0086038
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .050076{col 26}{space 2} .0237703{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0034406{col 67}{space 3} .0967114
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0246498{col 26}{space 2} .0200215{col 37}{space 1}   -1.23{col 46}{space 3}0.218{col 54}{space 4}-.0639303{col 67}{space 3} .0146307
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0090214{col 26}{space 2} .0219674{col 37}{space 1}    0.41{col 46}{space 3}0.681{col 54}{space 4}-.0340769{col 67}{space 3} .0521197
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0020058{col 26}{space 2} .0140221{col 37}{space 1}   -0.14{col 46}{space 3}0.886{col 54}{space 4} -.029516{col 67}{space 3} .0255044
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0020409{col 26}{space 2} .0005693{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0031578{col 67}{space 3}-.0009239
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0160047{col 26}{space 2} .0076569{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-.0310269{col 67}{space 3}-.0009825
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}  .021013{col 26}{space 2} .0208277{col 37}{space 1}    1.01{col 46}{space 3}0.313{col 54}{space 4}-.0198493{col 67}{space 3} .0618752
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0014914{col 26}{space 2} .0028615{col 37}{space 1}   -0.52{col 46}{space 3}0.602{col 54}{space 4}-.0071055{col 67}{space 3} .0041226
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0561014{col 26}{space 2} .0162157{col 37}{space 1}    3.46{col 46}{space 3}0.001{col 54}{space 4} .0242875{col 67}{space 3} .0879152
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0400946{col 26}{space 2} .0349397{col 37}{space 1}    1.15{col 46}{space 3}0.251{col 54}{space 4}-.0284542{col 67}{space 3} .1086434
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0107351{col 26}{space 2}  .039043{col 37}{space 1}    0.27{col 46}{space 3}0.783{col 54}{space 4} -.065864{col 67}{space 3} .0873341
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0078122{col 26}{space 2} .0343979{col 37}{space 1}   -0.23{col 46}{space 3}0.820{col 54}{space 4}-.0752979{col 67}{space 3} .0596736
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}  .063524{col 26}{space 2} .0493553{col 37}{space 1}    1.29{col 46}{space 3}0.198{col 54}{space 4} -.033307{col 67}{space 3} .1603551
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0070072{col 26}{space 2}  .032079{col 37}{space 1}    0.22{col 46}{space 3}0.827{col 54}{space 4}-.0559292{col 67}{space 3} .0699436
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}  .013346{col 26}{space 2}  .037102{col 37}{space 1}    0.36{col 46}{space 3}0.719{col 54}{space 4} -.059445{col 67}{space 3}  .086137
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2254522{col 26}{space 2} .0518181{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4} .1237893{col 67}{space 3}  .327115
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1233}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Opposition"

{txt}added macro:
             e(middle) : "{res:Opposition}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==3, robust

{txt}Linear regression                               Number of obs     = {res}     1,021
                                                {txt}{help j_robustsingular:F(16, 1003) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0490
                                                {txt}Root MSE          =    {res} .32542

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0336411{col 26}{space 2} .0322959{col 37}{space 1}    1.04{col 46}{space 3}0.298{col 54}{space 4}-.0297342{col 67}{space 3} .0970164
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0627474{col 26}{space 2} .0326404{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4} -.001304{col 67}{space 3} .1267988
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0260185{col 26}{space 2} .0321863{col 37}{space 1}    0.81{col 46}{space 3}0.419{col 54}{space 4}-.0371417{col 67}{space 3} .0891786
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0454488{col 26}{space 2} .0315888{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4} -.016539{col 67}{space 3} .1074365
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0078321{col 26}{space 2}  .021253{col 37}{space 1}   -0.37{col 46}{space 3}0.713{col 54}{space 4}-.0495376{col 67}{space 3} .0338734
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0019013{col 26}{space 2} .0008279{col 37}{space 1}   -2.30{col 46}{space 3}0.022{col 54}{space 4}-.0035258{col 67}{space 3}-.0002767
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0011928{col 26}{space 2} .0098079{col 37}{space 1}    0.12{col 46}{space 3}0.903{col 54}{space 4}-.0180535{col 67}{space 3} .0204392
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0483058{col 26}{space 2} .0248682{col 37}{space 1}   -1.94{col 46}{space 3}0.052{col 54}{space 4}-.0971054{col 67}{space 3} .0004939
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0105415{col 26}{space 2} .0039866{col 37}{space 1}   -2.64{col 46}{space 3}0.008{col 54}{space 4}-.0183645{col 67}{space 3}-.0027185
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1489054{col 26}{space 2} .0307597{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4} .0885447{col 67}{space 3} .2092662
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1590882{col 26}{space 2} .0354598{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4}-.2286721{col 67}{space 3}-.0895043
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} -.190457{col 26}{space 2} .0414952{col 37}{space 1}   -4.59{col 46}{space 3}0.000{col 54}{space 4}-.2718844{col 67}{space 3}-.1090297
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.1488178{col 26}{space 2} .0413406{col 37}{space 1}   -3.60{col 46}{space 3}0.000{col 54}{space 4}-.2299417{col 67}{space 3}-.0676939
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0795151{col 26}{space 2} .0501605{col 37}{space 1}   -1.59{col 46}{space 3}0.113{col 54}{space 4}-.1779467{col 67}{space 3} .0189165
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.1432943{col 26}{space 2} .0315328{col 37}{space 1}   -4.54{col 46}{space 3}0.000{col 54}{space 4}-.2051721{col 67}{space 3}-.0814164
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.1085215{col 26}{space 2} .0400707{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4}-.1871536{col 67}{space 3}-.0298894
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.1295239{col 26}{space 2} .0485919{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.2248774{col 67}{space 3}-.0341705
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4342941{col 26}{space 2} .0724863{col 37}{space 1}    5.99{col 46}{space 3}0.000{col 54}{space 4}  .292052{col 67}{space 3} .5765362
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1021}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Unaffiliated"

{txt}added macro:
             e(middle) : "{res:Unaffiliated}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==1 & order==2, robust
{txt}{p 0 6 2}note: {bf:r3} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       805
                                                {txt}F(16, 788)        =  {res}     1.35
                                                {txt}Prob > F          = {res}    0.1613
                                                {txt}R-squared         = {res}    0.0252
                                                {txt}Root MSE          =    {res} .33309

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0477457{col 26}{space 2} .0386793{col 37}{space 1}   -1.23{col 46}{space 3}0.217{col 54}{space 4}-.1236723{col 67}{space 3} .0281809
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0111907{col 26}{space 2} .0374338{col 37}{space 1}   -0.30{col 46}{space 3}0.765{col 54}{space 4}-.0846724{col 67}{space 3}  .062291
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0051368{col 26}{space 2} .0361646{col 37}{space 1}    0.14{col 46}{space 3}0.887{col 54}{space 4}-.0658534{col 67}{space 3} .0761271
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0017528{col 26}{space 2} .0381292{col 37}{space 1}   -0.05{col 46}{space 3}0.963{col 54}{space 4}-.0765996{col 67}{space 3}  .073094
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0205353{col 26}{space 2} .0250072{col 37}{space 1}    0.82{col 46}{space 3}0.412{col 54}{space 4}-.0285533{col 67}{space 3} .0696238
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0017077{col 26}{space 2} .0011067{col 37}{space 1}    1.54{col 46}{space 3}0.123{col 54}{space 4}-.0004648{col 67}{space 3} .0038801
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0074021{col 26}{space 2} .0110219{col 37}{space 1}   -0.67{col 46}{space 3}0.502{col 54}{space 4}-.0290378{col 67}{space 3} .0142335
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0004492{col 26}{space 2} .0326488{col 37}{space 1}   -0.01{col 46}{space 3}0.989{col 54}{space 4}-.0645382{col 67}{space 3} .0636397
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0038587{col 26}{space 2} .0048678{col 37}{space 1}   -0.79{col 46}{space 3}0.428{col 54}{space 4}-.0134141{col 67}{space 3} .0056966
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0963437{col 26}{space 2} .1014123{col 37}{space 1}    0.95{col 46}{space 3}0.342{col 54}{space 4}-.1027266{col 67}{space 3}  .295414
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1207317{col 26}{space 2} .0531061{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.2249779{col 67}{space 3}-.0164855
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r4 {c |}{col 14}{res}{space 2} -.006739{col 26}{space 2} .0452107{col 37}{space 1}   -0.15{col 46}{space 3}0.882{col 54}{space 4}-.0954868{col 67}{space 3} .0820087
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0044381{col 26}{space 2} .0616424{col 37}{space 1}   -0.07{col 46}{space 3}0.943{col 54}{space 4}-.1254407{col 67}{space 3} .1165646
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0598259{col 26}{space 2} .0407212{col 37}{space 1}   -1.47{col 46}{space 3}0.142{col 54}{space 4}-.1397608{col 67}{space 3}  .020109
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0980312{col 26}{space 2} .0502619{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-.1966943{col 67}{space 3} .0006319
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0632358{col 26}{space 2} .0521618{col 37}{space 1}   -1.21{col 46}{space 3}0.226{col 54}{space 4}-.1656284{col 67}{space 3} .0391568
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6681667{col 26}{space 2} .1181711{col 37}{space 1}    5.65{col 46}{space 3}0.000{col 54}{space 4} .4361993{col 67}{space 3} .9001341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:805}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Erdoğan"

{txt}added macro:
             e(middle) : "{res:Erdoğan}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==2 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       615
                                                {txt}F(16, 598)        =  {res}     2.63
                                                {txt}Prob > F          = {res}    0.0005
                                                {txt}R-squared         = {res}    0.0494
                                                {txt}Root MSE          =    {res} .24287

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0431066{col 26}{space 2} .0325969{col 37}{space 1}   -1.32{col 46}{space 3}0.187{col 54}{space 4}-.1071249{col 67}{space 3} .0209116
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .030898{col 26}{space 2} .0341781{col 37}{space 1}    0.90{col 46}{space 3}0.366{col 54}{space 4}-.0362257{col 67}{space 3} .0980216
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0436931{col 26}{space 2} .0302024{col 37}{space 1}   -1.45{col 46}{space 3}0.149{col 54}{space 4}-.1030086{col 67}{space 3} .0156225
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0221939{col 26}{space 2} .0311821{col 37}{space 1}   -0.71{col 46}{space 3}0.477{col 54}{space 4}-.0834337{col 67}{space 3} .0390459
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0290119{col 26}{space 2} .0203094{col 37}{space 1}    1.43{col 46}{space 3}0.154{col 54}{space 4}-.0108745{col 67}{space 3} .0688984
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0016098{col 26}{space 2} .0007555{col 37}{space 1}   -2.13{col 46}{space 3}0.034{col 54}{space 4}-.0030935{col 67}{space 3} -.000126
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0169716{col 26}{space 2} .0114188{col 37}{space 1}   -1.49{col 46}{space 3}0.138{col 54}{space 4}-.0393975{col 67}{space 3} .0054542
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0150261{col 26}{space 2}  .030954{col 37}{space 1}    0.49{col 46}{space 3}0.628{col 54}{space 4}-.0457656{col 67}{space 3} .0758178
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0000589{col 26}{space 2} .0041082{col 37}{space 1}   -0.01{col 46}{space 3}0.989{col 54}{space 4}-.0081272{col 67}{space 3} .0080093
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0488895{col 26}{space 2}  .021933{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0058145{col 67}{space 3} .0919645
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}  .023839{col 26}{space 2} .0573525{col 37}{space 1}    0.42{col 46}{space 3}0.678{col 54}{space 4}-.0887977{col 67}{space 3} .1364757
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0195811{col 26}{space 2} .0650066{col 37}{space 1}    0.30{col 46}{space 3}0.763{col 54}{space 4}-.1080879{col 67}{space 3} .1472501
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0211543{col 26}{space 2}  .056235{col 37}{space 1}   -0.38{col 46}{space 3}0.707{col 54}{space 4}-.1315964{col 67}{space 3} .0892878
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0486855{col 26}{space 2} .0714873{col 37}{space 1}    0.68{col 46}{space 3}0.496{col 54}{space 4}-.0917112{col 67}{space 3} .1890822
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0113988{col 26}{space 2} .0529504{col 37}{space 1}   -0.22{col 46}{space 3}0.830{col 54}{space 4}-.1153903{col 67}{space 3} .0925926
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0096657{col 26}{space 2} .0601372{col 37}{space 1}    0.16{col 46}{space 3}0.872{col 54}{space 4}-.1084402{col 67}{space 3} .1277716
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2199064{col 26}{space 2} .0726936{col 37}{space 1}    3.03{col 46}{space 3}0.003{col 54}{space 4} .0771407{col 67}{space 3} .3626722
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:615}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Opposition"

{txt}added macro:
             e(middle) : "{res:Opposition}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==3 & order==2, robust

{txt}Linear regression                               Number of obs     = {res}       518
                                                {txt}{help j_robustsingular:F(16, 500) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0665
                                                {txt}Root MSE          =    {res} .32803

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0583244{col 26}{space 2} .0440897{col 37}{space 1}    1.32{col 46}{space 3}0.186{col 54}{space 4}-.0282995{col 67}{space 3} .1449483
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .1095617{col 26}{space 2} .0453828{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0203972{col 67}{space 3} .1987262
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0830144{col 26}{space 2} .0459107{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0071873{col 67}{space 3} .1732161
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0757515{col 26}{space 2}  .042746{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}-.0082325{col 67}{space 3} .1597355
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0075952{col 26}{space 2} .0297397{col 37}{space 1}   -0.26{col 46}{space 3}0.799{col 54}{space 4}-.0660253{col 67}{space 3} .0508349
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0010602{col 26}{space 2} .0011287{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-.0032778{col 67}{space 3} .0011573
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0020049{col 26}{space 2}  .013567{col 37}{space 1}   -0.15{col 46}{space 3}0.883{col 54}{space 4}-.0286602{col 67}{space 3} .0246504
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0349885{col 26}{space 2} .0345721{col 37}{space 1}   -1.01{col 46}{space 3}0.312{col 54}{space 4}-.1029129{col 67}{space 3} .0329359
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0120608{col 26}{space 2} .0053807{col 37}{space 1}   -2.24{col 46}{space 3}0.025{col 54}{space 4}-.0226323{col 67}{space 3}-.0014893
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1703312{col 26}{space 2} .0414829{col 37}{space 1}    4.11{col 46}{space 3}0.000{col 54}{space 4} .0888289{col 67}{space 3} .2518336
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} -.154993{col 26}{space 2} .0514178{col 37}{space 1}   -3.01{col 46}{space 3}0.003{col 54}{space 4}-.2560146{col 67}{space 3}-.0539714
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.1720826{col 26}{space 2} .0590859{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4}-.2881697{col 67}{space 3}-.0559954
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0945588{col 26}{space 2} .0601834{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-.2128022{col 67}{space 3} .0236847
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0473016{col 26}{space 2} .0752793{col 37}{space 1}   -0.63{col 46}{space 3}0.530{col 54}{space 4}-.1952043{col 67}{space 3} .1006012
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.1238272{col 26}{space 2} .0446593{col 37}{space 1}   -2.77{col 46}{space 3}0.006{col 54}{space 4}-.2115703{col 67}{space 3}-.0360842
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.1099147{col 26}{space 2} .0573285{col 37}{space 1}   -1.92{col 46}{space 3}0.056{col 54}{space 4}-.2225492{col 67}{space 3} .0027197
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0484307{col 26}{space 2} .0744719{col 37}{space 1}   -0.65{col 46}{space 3}0.516{col 54}{space 4}-.1947471{col 67}{space 3} .0978857
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3704858{col 26}{space 2} .1015832{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .1709032{col 67}{space 3} .5700683
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:518}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Unaffiliated"

{txt}added macro:
             e(middle) : "{res:Unaffiliated}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==1 & order==1, robust
{txt}{p 0 6 2}note: {bf:r3} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       780
                                                {txt}F(16, 763)        =  {res}     1.93
                                                {txt}Prob > F          = {res}    0.0156
                                                {txt}R-squared         = {res}    0.0386
                                                {txt}Root MSE          =    {res} .34312

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.1219203{col 26}{space 2} .0399882{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.2004201{col 67}{space 3}-.0434204
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0484523{col 26}{space 2} .0368431{col 37}{space 1}   -1.32{col 46}{space 3}0.189{col 54}{space 4}-.1207782{col 67}{space 3} .0238736
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0139554{col 26}{space 2} .0373078{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.0871936{col 67}{space 3} .0592828
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0030362{col 26}{space 2} .0366378{col 37}{space 1}   -0.08{col 46}{space 3}0.934{col 54}{space 4} -.074959{col 67}{space 3} .0688867
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0439046{col 26}{space 2} .0269363{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0089734{col 67}{space 3} .0967826
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0001266{col 26}{space 2} .0013238{col 37}{space 1}    0.10{col 46}{space 3}0.924{col 54}{space 4}-.0024722{col 67}{space 3} .0027253
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0220113{col 26}{space 2} .0106046{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-.0428289{col 67}{space 3}-.0011937
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0133754{col 26}{space 2} .0360297{col 37}{space 1}    0.37{col 46}{space 3}0.711{col 54}{space 4}-.0573538{col 67}{space 3} .0841045
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0021013{col 26}{space 2}  .004927{col 37}{space 1}    0.43{col 46}{space 3}0.670{col 54}{space 4}-.0075708{col 67}{space 3} .0117733
{txt}{space 7}islam {c |}{col 14}{res}{space 2}  .062361{col 26}{space 2} .1230326{col 37}{space 1}    0.51{col 46}{space 3}0.612{col 54}{space 4}-.1791615{col 67}{space 3} .3038835
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1710144{col 26}{space 2} .0563578{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-.2816491{col 67}{space 3}-.0603797
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r4 {c |}{col 14}{res}{space 2}-.0925725{col 26}{space 2}  .049142{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4} -.189042{col 67}{space 3} .0038971
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0939478{col 26}{space 2} .0529998{col 37}{space 1}   -1.77{col 46}{space 3}0.077{col 54}{space 4}-.1979906{col 67}{space 3} .0100951
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0866034{col 26}{space 2} .0413166{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4} -.167711{col 67}{space 3}-.0054957
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0745194{col 26}{space 2} .0502079{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-.1730814{col 67}{space 3} .0240426
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0799839{col 26}{space 2} .0577732{col 37}{space 1}   -1.38{col 46}{space 3}0.167{col 54}{space 4}-.1933972{col 67}{space 3} .0334294
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8102877{col 26}{space 2} .1454815{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} .5246962{col 67}{space 3} 1.095879
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:780}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Erdoğan"

{txt}added macro:
             e(middle) : "{res:Erdoğan}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==2 & order==1, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       618
                                                {txt}F(16, 601)        =  {res}     2.71
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0722
                                                {txt}Root MSE          =    {res} .23869

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0513608{col 26}{space 2} .0260613{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4} -.102543{col 67}{space 3}-.0001786
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .070258{col 26}{space 2} .0338804{col 37}{space 1}    2.07{col 46}{space 3}0.039{col 54}{space 4} .0037197{col 67}{space 3} .1367962
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} -.010056{col 26}{space 2}  .026728{col 37}{space 1}   -0.38{col 46}{space 3}0.707{col 54}{space 4}-.0625476{col 67}{space 3} .0424356
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0430855{col 26}{space 2} .0320629{col 37}{space 1}    1.34{col 46}{space 3}0.180{col 54}{space 4}-.0198835{col 67}{space 3} .1060545
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0320713{col 26}{space 2} .0193117{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4} -.069998{col 67}{space 3} .0058554
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0025434{col 26}{space 2} .0008841{col 37}{space 1}   -2.88{col 46}{space 3}0.004{col 54}{space 4}-.0042797{col 67}{space 3}-.0008071
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0149356{col 26}{space 2} .0105767{col 37}{space 1}   -1.41{col 46}{space 3}0.158{col 54}{space 4}-.0357073{col 67}{space 3} .0058361
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0282266{col 26}{space 2} .0286276{col 37}{space 1}    0.99{col 46}{space 3}0.325{col 54}{space 4}-.0279958{col 67}{space 3}  .084449
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0022867{col 26}{space 2} .0040247{col 37}{space 1}   -0.57{col 46}{space 3}0.570{col 54}{space 4} -.010191{col 67}{space 3} .0056175
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0608082{col 26}{space 2} .0247944{col 37}{space 1}    2.45{col 46}{space 3}0.014{col 54}{space 4} .0121139{col 67}{space 3} .1095024
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0366398{col 26}{space 2} .0645927{col 37}{space 1}   -0.57{col 46}{space 3}0.571{col 54}{space 4}-.1634946{col 67}{space 3}  .090215
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0844409{col 26}{space 2} .0668168{col 37}{space 1}   -1.26{col 46}{space 3}0.207{col 54}{space 4}-.2156636{col 67}{space 3} .0467819
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0795875{col 26}{space 2} .0638531{col 37}{space 1}   -1.25{col 46}{space 3}0.213{col 54}{space 4}-.2049898{col 67}{space 3} .0458148
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0629849{col 26}{space 2}   .06151{col 37}{space 1}   -1.02{col 46}{space 3}0.306{col 54}{space 4}-.1837856{col 67}{space 3} .0578158
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} -.070504{col 26}{space 2} .0656645{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-.1994637{col 67}{space 3} .0584558
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0794404{col 26}{space 2}  .069636{col 37}{space 1}   -1.14{col 46}{space 3}0.254{col 54}{space 4}-.2161999{col 67}{space 3} .0573192
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3175486{col 26}{space 2} .0896917{col 37}{space 1}    3.54{col 46}{space 3}0.000{col 54}{space 4} .1414014{col 67}{space 3} .4936958
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est8{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:618}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Opposition"

{txt}added macro:
             e(middle) : "{res:Opposition}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==3 & order==1, robust
{txt}{p 0 6 2}note: {bf:r3} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       503
                                                {txt}F(16, 486)        =  {res}     1.84
                                                {txt}Prob > F          = {res}    0.0241
                                                {txt}R-squared         = {res}    0.0498
                                                {txt}Root MSE          =    {res} .32428

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0091322{col 26}{space 2} .0485957{col 37}{space 1}    0.19{col 46}{space 3}0.851{col 54}{space 4}-.0863514{col 67}{space 3} .1046157
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0146971{col 26}{space 2}   .04843{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4}-.0804609{col 67}{space 3} .1098551
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0145399{col 26}{space 2} .0471176{col 37}{space 1}   -0.31{col 46}{space 3}0.758{col 54}{space 4}-.1071191{col 67}{space 3} .0780394
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0165213{col 26}{space 2} .0484539{col 37}{space 1}    0.34{col 46}{space 3}0.733{col 54}{space 4}-.0786837{col 67}{space 3} .1117264
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0091784{col 26}{space 2} .0307359{col 37}{space 1}   -0.30{col 46}{space 3}0.765{col 54}{space 4}  -.06957{col 67}{space 3} .0512133
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0027343{col 26}{space 2} .0012146{col 37}{space 1}   -2.25{col 46}{space 3}0.025{col 54}{space 4}-.0051208{col 67}{space 3}-.0003478
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0066284{col 26}{space 2} .0146231{col 37}{space 1}    0.45{col 46}{space 3}0.651{col 54}{space 4}-.0221039{col 67}{space 3} .0353607
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0633923{col 26}{space 2} .0360191{col 37}{space 1}   -1.76{col 46}{space 3}0.079{col 54}{space 4}-.1341646{col 67}{space 3}   .00738
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0096827{col 26}{space 2} .0059668{col 37}{space 1}   -1.62{col 46}{space 3}0.105{col 54}{space 4}-.0214066{col 67}{space 3} .0020413
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1322118{col 26}{space 2} .0453956{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0430159{col 67}{space 3} .2214077
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0594093{col 26}{space 2} .0587721{col 37}{space 1}    1.01{col 46}{space 3}0.313{col 54}{space 4}-.0560695{col 67}{space 3} .1748881
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r4 {c |}{col 14}{res}{space 2} .0212296{col 26}{space 2}   .06485{col 37}{space 1}    0.33{col 46}{space 3}0.744{col 54}{space 4}-.1061914{col 67}{space 3} .1486506
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1202504{col 26}{space 2} .0772562{col 37}{space 1}    1.56{col 46}{space 3}0.120{col 54}{space 4}-.0315469{col 67}{space 3} .2720477
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}  .053621{col 26}{space 2}  .055078{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0545994{col 67}{space 3} .1618414
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .1100679{col 26}{space 2} .0651864{col 37}{space 1}    1.69{col 46}{space 3}0.092{col 54}{space 4}-.0180141{col 67}{space 3} .2381499
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0275841{col 26}{space 2} .0695893{col 37}{space 1}    0.40{col 46}{space 3}0.692{col 54}{space 4} -.109149{col 67}{space 3} .1643172
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2666697{col 26}{space 2} .1004298{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} .0693395{col 67}{space 3} .4639999
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est9{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:503}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Unaffiliated"

{txt}added macro:
             e(middle) : "{res:Unaffiliated}"

{com}. esttab using "`drive'/HTEmiddlereduced.tex", replace ///
>         keep(t2 t3 t4 t5 _cons) ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(controls sample middle i, label("Controls" "Sample" "Partisanship" "Observations")) ///
>         title("Summary of Treatment Effects Conditional on Partisanship \label{c -(}tab:HTEmiddlereduced{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/HTEmiddlereduced.tex"'})

{com}. 
. 
. 
. *********************************
. *** Table F1: Summary Statistics
. *********************************
. estpost tabstat o1_std o2_std akp o6_std dv o8_std o4_std rpp o7_std female age education govt_emp income islam r2 r3 r4 r5 r6 r7 r8, c(stat) stat(min max mean sd)

{txt}Summary statistics: min max mean sd
     for variables: o1_std o2_std akp o6_std dv o8_std o4_std rpp o7_std female age education govt_emp income islam r2 r3 r4 r5 r6 r7 r8

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:o1_std}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .4126248}}}{space 1}{space 1}{ralign 9:{res:{sf: .4014361}}}{space 1}
{space 0}{space 0}{ralign 12:o2_std}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .4024584}}}{space 1}{space 1}{ralign 9:{res:{sf: .4312022}}}{space 1}
{space 0}{space 0}{ralign 12:akp}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .3248821}}}{space 1}{space 1}{ralign 9:{res:{sf: .4683998}}}{space 1}
{space 0}{space 0}{ralign 12:o6_std}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .2485621}}}{space 1}{space 1}{ralign 9:{res:{sf: .3647586}}}{space 1}
{space 0}{space 0}{ralign 12:dv}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}{space 1}{ralign 9:{res:{sf: .9352091}}}{space 1}{space 1}{ralign 9:{res:{sf: .9425448}}}{space 1}
{space 0}{space 0}{ralign 12:o8_std}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:  .376872}}}{space 1}{space 1}{ralign 9:{res:{sf: .3948503}}}{space 1}
{space 0}{space 0}{ralign 12:o4_std}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .5384049}}}{space 1}{space 1}{ralign 9:{res:{sf: .4313058}}}{space 1}
{space 0}{space 0}{ralign 12:rpp}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .3735259}}}{space 1}{space 1}{ralign 9:{res:{sf: .4838112}}}{space 1}
{space 0}{space 0}{ralign 12:o7_std}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .3230393}}}{space 1}{space 1}{ralign 9:{res:{sf: .3844434}}}{space 1}
{space 0}{space 0}{ralign 12:female}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .4757926}}}{space 1}{space 1}{ralign 9:{res:{sf: .4994672}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       18}}}{space 1}{space 1}{ralign 9:{res:{sf:       93}}}{space 1}{space 1}{ralign 9:{res:{sf: 36.54651}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.03831}}}{space 1}
{space 0}{space 0}{ralign 12:education}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        6}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.259275}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.172165}}}{space 1}
{space 0}{space 0}{ralign 12:govt_emp}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .2103165}}}{space 1}{space 1}{ralign 9:{res:{sf: .4075812}}}{space 1}
{space 0}{space 0}{ralign 12:income}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:       11}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.357301}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.882561}}}{space 1}
{space 0}{space 0}{ralign 12:islam}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .9106754}}}{space 1}{space 1}{ralign 9:{res:{sf: .2852428}}}{space 1}
{space 0}{space 0}{ralign 12:r2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .1431592}}}{space 1}{space 1}{ralign 9:{res:{sf: .3502722}}}{space 1}
{space 0}{space 0}{ralign 12:r3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .0820469}}}{space 1}{space 1}{ralign 9:{res:{sf: .2744652}}}{space 1}
{space 0}{space 0}{ralign 12:r4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .1336435}}}{space 1}{space 1}{ralign 9:{res:{sf: .3403048}}}{space 1}
{space 0}{space 0}{ralign 12:r5}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .0672447}}}{space 1}{space 1}{ralign 9:{res:{sf: .2504717}}}{space 1}
{space 0}{space 0}{ralign 12:r6}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .3465849}}}{space 1}{space 1}{ralign 9:{res:{sf: .4759325}}}{space 1}
{space 0}{space 0}{ralign 12:r7}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .1226475}}}{space 1}{space 1}{ralign 9:{res:{sf: .3280668}}}{space 1}
{space 0}{space 0}{ralign 12:r8}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:  .078875}}}{space 1}{space 1}{ralign 9:{res:{sf: .2695721}}}{space 1}

{com}. ereturn list

{txt}scalars:
                  e(N) =  {res}4729

{txt}macros:
                e(cmd) : "{res}estpost{txt}"
             e(subcmd) : "{res}tabstat{txt}"
              e(stats) : "{res}min max mean sd{txt}"
               e(vars) : "{res}o1_std o2_std akp o6_std dv o8_std o4_std rpp o7_std female a{txt}.."

matrices:
                e(min) : {res} 1 x 22
                {txt}e(max) : {res} 1 x 22
               {txt}e(mean) : {res} 1 x 22
                 {txt}e(sd) : {res} 1 x 22
{txt}
{com}. esttab . using "`drive'/summary.tex", replace ///
>         cells("min max mean(fmt(%6.2fc)) sd(fmt(%6.2fc))") nonumber ///
>         nomtitle nonote noobs label collabels("Minimum" "Maximum" "Mean" "Standard deviation") ///
>         title("Summary Statistics \label{c -(}tab:summary{c )-}")
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/summary.tex"'})

{com}. 
. 
. 
. *********************************************************************
. *** Table H1: Covariate Balance Between Control and Treatment Groups
. *********************************************************************
. global covar "female age education govt_emp income islam r1 r2 r3 r4 r5 r6 r7 r8"
{txt}
{com}. 
. sum $covar

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}      4,668    .4757926    .4994672          0          1
{txt}{space 9}age {c |}{res}      4,655    36.54651    12.03831         18         93
{txt}{space 3}education {c |}{res}      4,636    4.259275    1.172165          1          6
{txt}{space 4}govt_emp {c |}{res}      4,265    .2103165    .4075812          0          1
{txt}{space 6}income {c |}{res}      4,506    7.357301    2.882561          1         11
{txt}{hline 13}{c +}{hline 57}
{space 7}islam {c |}{res}      4,590    .9106754    .2852428          0          1
{txt}{space 10}r1 {c |}{res}      4,729    .0257983    .1585498          0          1
{txt}{space 10}r2 {c |}{res}      4,729    .1431592    .3502722          0          1
{txt}{space 10}r3 {c |}{res}      4,729    .0820469    .2744652          0          1
{txt}{space 10}r4 {c |}{res}      4,729    .1336435    .3403048          0          1
{txt}{hline 13}{c +}{hline 57}
{space 10}r5 {c |}{res}      4,729    .0672447    .2504717          0          1
{txt}{space 10}r6 {c |}{res}      4,729    .3465849    .4759325          0          1
{txt}{space 10}r7 {c |}{res}      4,729    .1226475    .3280668          0          1
{txt}{space 10}r8 {c |}{res}      4,729     .078875    .2695721          0          1
{txt}
{com}. tabstat $covar, by(treatment) nototal

{txt}Summary statistics: Mean
Group variable: treatment (Control/treatment group)

{ralign 10:treatment} {...}
{c |}{...}
    female       age  educat~n  govt_emp    income     islam        r1
{hline 11}{c +}{hline 70}
{ralign 10:Control} {...}
{c |}{...}
 {res} .4614525  36.55592  4.223085  .2102066  7.363636  .9106145         0
{txt}{ralign 10:Force} {...}
{c |}{...}
 {res} .4905451  36.17054  4.241379   .216445  7.356086  .9187082  .0011074
{txt}{ralign 10:Minister} {...}
{c |}{...}
 {res} .4614525  36.44235  4.278396  .2054958  7.338219  .9118304  .0022173
{txt}{ralign 10:Opposition} {...}
{c |}{...}
 {res}  .473743  36.93348  4.216667  .2009627  7.328054   .901676         0
{txt}{ralign 10:Private} {...}
{c |}{...}
 {res} .4816054  36.98331  4.377778  .1907032  7.531215  .9140625         0
{txt}{hline 11}{c BT}{hline 70}

{ralign 10:treatment} {...}
{c |}{...}
        r2        r3        r4        r5        r6        r7        r8
{hline 11}{c +}{hline 70}
{ralign 10:Control} {...}
{c |}{...}
 {res} .1526549  .0995575  .1382743  .0608407  .3451327   .130531  .0730088
{txt}{ralign 10:Force} {...}
{c |}{...}
 {res} .1550388  .0863787  .1406423  .0575858  .3410853  .1306755  .0874862
{txt}{ralign 10:Minister} {...}
{c |}{...}
 {res} .1441242   .075388  .1419069  .0742794  .3636364  .1197339   .078714
{txt}{ralign 10:Opposition} {...}
{c |}{...}
 {res} .1452328  .0798226  .1341463  .0898004  .3470067  .1274945  .0764967
{txt}{ralign 10:Private} {...}
{c |}{...}
 {res} .1366667  .0811111  .1344444       .06  .3866667  .1166667  .0844444
{txt}{hline 11}{c BT}{hline 70}

{com}. 
. mat Ftest=J(14,1,0)
{txt}
{com}. local j=1
{txt}
{com}. qui foreach var of varlist $covar {c -(}
{txt}
{com}. mat list Ftest
{res}
{txt}Ftest[14,1]
            c1
 r1 {res} .21745752
{txt} r2 {res} .49715539
{txt} r3 {res} .74331391
{txt} r4 {res} .75724793
{txt} r5 {res} .95572879
{txt} r6 {res} .53987812
{txt} r7 {res} .31736444
{txt} r8 {res} .88838583
{txt} r9 {res} .33493332
{txt}r10 {res} .88455662
{txt}r11 {res} .76956954
{txt}r12 {res} .85629226
{txt}r13 {res} .99272961
{txt}r14 {res} .25753839
{reset}
{com}. 
. 
. 
. ****************************************************************
. *** Table I1: Average Treatment Effects on Approval for Erdogan
. ****************************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8, robust

{txt}Linear regression                               Number of obs     = {res}     3,839
                                                {txt}{help j_robustsingular:F(16, 3821) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0664
                                                {txt}Root MSE          =    {res} .38859

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0386804{col 26}{space 2}  .020216{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.0783155{col 67}{space 3} .0009547
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .007164{col 26}{space 2} .0198044{col 37}{space 1}    0.36{col 46}{space 3}0.718{col 54}{space 4}-.0316643{col 67}{space 3} .0459922
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} -.023032{col 26}{space 2}  .019885{col 37}{space 1}   -1.16{col 46}{space 3}0.247{col 54}{space 4}-.0620183{col 67}{space 3} .0159542
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0039108{col 26}{space 2} .0199158{col 37}{space 1}    0.20{col 46}{space 3}0.844{col 54}{space 4}-.0351358{col 67}{space 3} .0429575
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0192167{col 26}{space 2} .0129906{col 37}{space 1}    1.48{col 46}{space 3}0.139{col 54}{space 4}-.0062524{col 67}{space 3} .0446858
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0018441{col 26}{space 2} .0005467{col 37}{space 1}   -3.37{col 46}{space 3}0.001{col 54}{space 4} -.002916{col 67}{space 3}-.0007721
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0265398{col 26}{space 2} .0060839{col 37}{space 1}   -4.36{col 46}{space 3}0.000{col 54}{space 4}-.0384677{col 67}{space 3}-.0146119
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0248349{col 26}{space 2} .0170059{col 37}{space 1}   -1.46{col 46}{space 3}0.144{col 54}{space 4}-.0581763{col 67}{space 3} .0085066
{txt}{space 6}income {c |}{col 14}{res}{space 2} -.005004{col 26}{space 2} .0024964{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4}-.0098985{col 67}{space 3}-.0001096
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2795518{col 26}{space 2} .0171696{col 37}{space 1}   16.28{col 46}{space 3}0.000{col 54}{space 4} .2458892{col 67}{space 3} .3132144
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1288057{col 26}{space 2}  .022063{col 37}{space 1}   -5.84{col 46}{space 3}0.000{col 54}{space 4} -.172062{col 67}{space 3}-.0855493
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0465764{col 26}{space 2}  .026936{col 37}{space 1}   -1.73{col 46}{space 3}0.084{col 54}{space 4}-.0993867{col 67}{space 3} .0062338
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0259661{col 26}{space 2} .0232224{col 37}{space 1}   -1.12{col 46}{space 3}0.264{col 54}{space 4}-.0714956{col 67}{space 3} .0195634
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} -.003378{col 26}{space 2} .0292397{col 37}{space 1}   -0.12{col 46}{space 3}0.908{col 54}{space 4} -.060705{col 67}{space 3}  .053949
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0707863{col 26}{space 2} .0182282{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-.1065242{col 67}{space 3}-.0350484
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0463971{col 26}{space 2} .0237092{col 37}{space 1}   -1.96{col 46}{space 3}0.050{col 54}{space 4}-.0928809{col 67}{space 3} .0000868
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0335174{col 26}{space 2} .0281405{col 37}{space 1}   -1.19{col 46}{space 3}0.234{col 54}{space 4}-.0886893{col 67}{space 3} .0216545
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4463419{col 26}{space 2} .0457796{col 37}{space 1}    9.75{col 46}{space 3}0.000{col 54}{space 4}  .356587{col 67}{space 3} .5360967
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3839}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if order==2, robust

{txt}Linear regression                               Number of obs     = {res}     1,938
                                                {txt}{help j_robustsingular:F(16, 1920) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0710
                                                {txt}Root MSE          =    {res}   .388

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}  .005132{col 26}{space 2} .0287502{col 37}{space 1}    0.18{col 46}{space 3}0.858{col 54}{space 4}-.0512529{col 67}{space 3} .0615169
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0202224{col 26}{space 2} .0278934{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-.0344821{col 67}{space 3}  .074927
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0124666{col 26}{space 2} .0282508{col 37}{space 1}    0.44{col 46}{space 3}0.659{col 54}{space 4}-.0429388{col 67}{space 3}  .067872
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}   .00403{col 26}{space 2} .0279097{col 37}{space 1}    0.14{col 46}{space 3}0.885{col 54}{space 4}-.0507064{col 67}{space 3} .0587665
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0117127{col 26}{space 2} .0181553{col 37}{space 1}    0.65{col 46}{space 3}0.519{col 54}{space 4}-.0238936{col 67}{space 3}  .047319
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0010726{col 26}{space 2}  .000749{col 37}{space 1}   -1.43{col 46}{space 3}0.152{col 54}{space 4}-.0025416{col 67}{space 3} .0003964
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0207856{col 26}{space 2} .0086483{col 37}{space 1}   -2.40{col 46}{space 3}0.016{col 54}{space 4}-.0377465{col 67}{space 3}-.0038246
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0174483{col 26}{space 2} .0233177{col 37}{space 1}   -0.75{col 46}{space 3}0.454{col 54}{space 4}-.0631789{col 67}{space 3} .0282823
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0061806{col 26}{space 2}  .003477{col 37}{space 1}   -1.78{col 46}{space 3}0.076{col 54}{space 4}-.0129996{col 67}{space 3} .0006384
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2900803{col 26}{space 2} .0227916{col 37}{space 1}   12.73{col 46}{space 3}0.000{col 54}{space 4} .2453814{col 67}{space 3} .3347792
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1198683{col 26}{space 2} .0311512{col 37}{space 1}   -3.85{col 46}{space 3}0.000{col 54}{space 4} -.180962{col 67}{space 3}-.0587747
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} -.032791{col 26}{space 2} .0382227{col 37}{space 1}   -0.86{col 46}{space 3}0.391{col 54}{space 4}-.1077533{col 67}{space 3} .0421713
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0017427{col 26}{space 2} .0329879{col 37}{space 1}   -0.05{col 46}{space 3}0.958{col 54}{space 4}-.0664386{col 67}{space 3} .0629532
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0109477{col 26}{space 2} .0441451{col 37}{space 1}   -0.25{col 46}{space 3}0.804{col 54}{space 4}-.0975252{col 67}{space 3} .0756297
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0753255{col 26}{space 2} .0254864{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.1253095{col 67}{space 3}-.0253415
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0681132{col 26}{space 2} .0328141{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-.1324682{col 67}{space 3}-.0037582
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0110898{col 26}{space 2} .0391511{col 37}{space 1}    0.28{col 46}{space 3}0.777{col 54}{space 4}-.0656933{col 67}{space 3} .0878728
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3814286{col 26}{space 2} .0645226{col 37}{space 1}    5.91{col 46}{space 3}0.000{col 54}{space 4} .2548868{col 67}{space 3} .5079704
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1938}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if order==1, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,901
                                                {txt}F(16, 1884)       =  {res}    12.32
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0721
                                                {txt}Root MSE          =    {res} .38857

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} -.083477{col 26}{space 2} .0285262{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.1394231{col 67}{space 3}-.0275308
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0053059{col 26}{space 2} .0282556{col 37}{space 1}   -0.19{col 46}{space 3}0.851{col 54}{space 4}-.0607216{col 67}{space 3} .0501097
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0584508{col 26}{space 2} .0279217{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.1132116{col 67}{space 3}-.0036901
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0028792{col 26}{space 2} .0285697{col 37}{space 1}    0.10{col 46}{space 3}0.920{col 54}{space 4}-.0531525{col 67}{space 3} .0589108
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0259736{col 26}{space 2} .0186108{col 37}{space 1}    1.40{col 46}{space 3}0.163{col 54}{space 4}-.0105263{col 67}{space 3} .0624735
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0028516{col 26}{space 2} .0007965{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0044138{col 67}{space 3}-.0012894
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.032857{col 26}{space 2} .0085749{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.0496742{col 67}{space 3}-.0160397
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0312024{col 26}{space 2} .0251338{col 37}{space 1}   -1.24{col 46}{space 3}0.215{col 54}{space 4}-.0804954{col 67}{space 3} .0180905
{txt}{space 6}income {c |}{col 14}{res}{space 2}  -.00362{col 26}{space 2} .0035927{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}-.0106661{col 67}{space 3} .0034262
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2701949{col 26}{space 2}  .026042{col 37}{space 1}   10.38{col 46}{space 3}0.000{col 54}{space 4} .2191207{col 67}{space 3}  .321269
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1361741{col 26}{space 2} .0405958{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4}-.2157917{col 67}{space 3}-.0565566
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0646129{col 26}{space 2} .0466803{col 37}{space 1}   -1.38{col 46}{space 3}0.166{col 54}{space 4}-.1561634{col 67}{space 3} .0269376
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0540477{col 26}{space 2} .0417237{col 37}{space 1}   -1.30{col 46}{space 3}0.195{col 54}{space 4}-.1358771{col 67}{space 3} .0277817
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0685149{col 26}{space 2} .0373185{col 37}{space 1}   -1.84{col 46}{space 3}0.067{col 54}{space 4}-.1417048{col 67}{space 3} .0046749
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0253662{col 26}{space 2} .0432731{col 37}{space 1}   -0.59{col 46}{space 3}0.558{col 54}{space 4}-.1102344{col 67}{space 3} .0595021
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0827082{col 26}{space 2} .0477035{col 37}{space 1}   -1.73{col 46}{space 3}0.083{col 54}{space 4}-.1762655{col 67}{space 3} .0108491
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .521944{col 26}{space 2} .0671329{col 37}{space 1}    7.77{col 46}{space 3}0.000{col 54}{space 4} .3902812{col 67}{space 3} .6536067
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1901}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. esttab using "`drive'/ATEfull.tex", replace ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(sample i, label("Sample" "Observations")) ///
>         title("Average Treatment Effects on Approval for Erdoğan \label{c -(}tab:ATEfull{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/ATEfull.tex"'})

{com}. 
. 
. 
. *************************************************************************************
. *** Table I2: Average Treatment Effects on Approval for Erdogan (without covariates)
. *************************************************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std t2-t5, robust

{txt}Linear regression                               Number of obs     = {res}     4,206
                                                {txt}F(4, 4201)        =  {res}     2.01
                                                {txt}Prob > F          = {res}    0.0906
                                                {txt}R-squared         = {res}    0.0019
                                                {txt}Root MSE          =    {res} .40125

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0414166{col 26}{space 2} .0197178{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.0800738{col 67}{space 3}-.0027593
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0062213{col 26}{space 2} .0194206{col 37}{space 1}    0.32{col 46}{space 3}0.749{col 54}{space 4}-.0318534{col 67}{space 3} .0442961
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0245535{col 26}{space 2}  .019698{col 37}{space 1}   -1.25{col 46}{space 3}0.213{col 54}{space 4}-.0631719{col 67}{space 3}  .014065
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0055702{col 26}{space 2} .0196895{col 37}{space 1}   -0.28{col 46}{space 3}0.777{col 54}{space 4} -.044172{col 67}{space 3} .0330315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4255702{col 26}{space 2} .0139572{col 37}{space 1}   30.49{col 46}{space 3}0.000{col 54}{space 4} .3982068{col 67}{space 3} .4529336
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:4206}"

{com}. estadd local controls "No"

{txt}added macro:
           e(controls) : "{res:No}"

{com}. eststo: reg o1_std t2-t5 if order==2, robust

{txt}Linear regression                               Number of obs     = {res}     2,132
                                                {txt}F(4, 2127)        =  {res}     0.39
                                                {txt}Prob > F          = {res}    0.8178
                                                {txt}R-squared         = {res}    0.0007
                                                {txt}Root MSE          =    {res} .40114

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0011976{col 26}{space 2} .0279054{col 37}{space 1}   -0.04{col 46}{space 3}0.966{col 54}{space 4}-.0559223{col 67}{space 3} .0535271
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0187127{col 26}{space 2} .0272131{col 37}{space 1}    0.69{col 46}{space 3}0.492{col 54}{space 4}-.0346543{col 67}{space 3} .0720797
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0087235{col 26}{space 2} .0278084{col 37}{space 1}    0.31{col 46}{space 3}0.754{col 54}{space 4}-.0458109{col 67}{space 3} .0632579
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0130848{col 26}{space 2} .0275566{col 37}{space 1}   -0.47{col 46}{space 3}0.635{col 54}{space 4}-.0671254{col 67}{space 3} .0409558
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4180622{col 26}{space 2} .0196973{col 37}{space 1}   21.22{col 46}{space 3}0.000{col 54}{space 4} .3794342{col 67}{space 3} .4566902
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:2132}"

{com}. estadd local controls "No"

{txt}added macro:
           e(controls) : "{res:No}"

{com}. eststo: reg o1_std t2-t5 if order==1, robust

{txt}Linear regression                               Number of obs     = {res}     2,074
                                                {txt}F(4, 2069)        =  {res}     3.94
                                                {txt}Prob > F          = {res}    0.0034
                                                {txt}R-squared         = {res}    0.0075
                                                {txt}Root MSE          =    {res} .40079

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0824044{col 26}{space 2} .0277934{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.1369104{col 67}{space 3}-.0278984
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0064186{col 26}{space 2} .0277476{col 37}{space 1}   -0.23{col 46}{space 3}0.817{col 54}{space 4}-.0608348{col 67}{space 3} .0479976
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0581325{col 26}{space 2} .0278764{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-.1128013{col 67}{space 3}-.0034637
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}  .003142{col 26}{space 2} .0281587{col 37}{space 1}    0.11{col 46}{space 3}0.911{col 54}{space 4}-.0520803{col 67}{space 3} .0583642
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4331325{col 26}{space 2} .0197964{col 37}{space 1}   21.88{col 46}{space 3}0.000{col 54}{space 4} .3943096{col 67}{space 3} .4719555
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:2074}"

{com}. estadd local controls "No"

{txt}added macro:
           e(controls) : "{res:No}"

{com}. esttab using "`drive'/ATEnocovariates.tex", replace ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(sample i, label("Sample" "Observations")) ///
>         title("Average Treatment Effects on Approval for Erdoğan \label{c -(}tab:ATEnocovariates{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/ATEnocovariates.tex"'})

{com}. 
. 
. 
. ***************************************************************************************************
. *** Table I3: Average Treatment Effects on Approval for Erdogan with Post-Strategification Weights
. ***************************************************************************************************
. preserve
{txt}
{com}.         est clear
{res}{txt}
{com}.         import delimited using "GSA_PoP_Weights.csv", clear
{res}{txt}(encoding automatically selected: UTF-8)
{res}{text}(103 vars, 3,839 obs)

{com}.         label var o1_std "Erdoğan approval"
{txt}
{com}.         label var t2 "Force majeure"
{txt}
{com}.         label var t3 "Minister"
{txt}
{com}.         label var t4 "Opposition"
{txt}
{com}.         label var t5 "Private companies"
{txt}
{com}.         label var female "Female"
{txt}
{com}.         label var age "Age"
{txt}
{com}.         label var education "Education"
{txt}
{com}.         label var govt_emp "Public sector employee"
{txt}
{com}.         label var income "Income"
{txt}
{com}.         label var islam "Islam"
{txt}
{com}.         label var r2 "Aegean"
{txt}
{com}.         label var r3 "Black Sea"
{txt}
{com}.         label var r4 "Central Anatolia"
{txt}
{com}.         label var r5 "Eastern Anatolia"
{txt}
{com}.         label var r6 "Marmara"
{txt}
{com}.         label var r7 "Mediterranean"
{txt}
{com}.         label var r8 "Southeastern Anatolia"
{txt}
{com}.         replace weight_province="." if weight_province=="NA"
{txt}(1 real change made)

{com}.         destring weight_province, replace
{txt}weight_province: all characters numeric; {res}replaced {txt}as {res}double
{txt}(1 missing value generated)
{res}{txt}
{com}.         gen weight_comp=weight_gender*weight_age*weight_edu*weight_province*weight_religion
{txt}(1 missing value generated)

{com}.         eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 [pweight=1/weight_comp], robust
{txt}(sum of wgt is 2.74440128367e-06)
{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,838
                                                {txt}F(16, 3821)       =  {res}    36.48
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1876
                                                {txt}Root MSE          =    {res} .34243

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0244883{col 26}{space 2} .0232326{col 37}{space 1}   -1.05{col 46}{space 3}0.292{col 54}{space 4}-.0700377{col 67}{space 3} .0210612
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0220638{col 26}{space 2} .0226953{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0224323{col 67}{space 3} .0665599
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0241796{col 26}{space 2} .0253254{col 37}{space 1}   -0.95{col 46}{space 3}0.340{col 54}{space 4}-.0738321{col 67}{space 3} .0254729
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0079863{col 26}{space 2} .0213107{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.0497678{col 67}{space 3} .0337951
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0131501{col 26}{space 2} .0147988{col 37}{space 1}    0.89{col 46}{space 3}0.374{col 54}{space 4}-.0158642{col 67}{space 3} .0421643
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0022766{col 26}{space 2}  .000756{col 37}{space 1}   -3.01{col 46}{space 3}0.003{col 54}{space 4}-.0037588{col 67}{space 3}-.0007944
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.007053{col 26}{space 2} .0089828{col 37}{space 1}   -0.79{col 46}{space 3}0.432{col 54}{space 4}-.0246645{col 67}{space 3} .0105584
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0045977{col 26}{space 2} .0195133{col 37}{space 1}    0.24{col 46}{space 3}0.814{col 54}{space 4}-.0336597{col 67}{space 3} .0428551
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0058364{col 26}{space 2}  .003153{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-.0120182{col 67}{space 3} .0003453
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3176272{col 26}{space 2} .0163098{col 37}{space 1}   19.47{col 46}{space 3}0.000{col 54}{space 4} .2856505{col 67}{space 3}  .349604
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0870482{col 26}{space 2} .0366998{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-.1590012{col 67}{space 3}-.0150952
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0691466{col 26}{space 2} .0377137{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-.1430875{col 67}{space 3} .0047943
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0672258{col 26}{space 2} .0361415{col 37}{space 1}   -1.86{col 46}{space 3}0.063{col 54}{space 4}-.1380843{col 67}{space 3} .0036327
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0709826{col 26}{space 2}  .032455{col 37}{space 1}   -2.19{col 46}{space 3}0.029{col 54}{space 4}-.1346134{col 67}{space 3}-.0073518
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0303386{col 26}{space 2} .0378181{col 37}{space 1}   -0.80{col 46}{space 3}0.422{col 54}{space 4}-.1044842{col 67}{space 3} .0438069
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0383021{col 26}{space 2} .0398656{col 37}{space 1}   -0.96{col 46}{space 3}0.337{col 54}{space 4} -.116462{col 67}{space 3} .0398578
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3308953{col 26}{space 2}  .062799{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} .2077726{col 67}{space 3}  .454018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}.         estadd local i`e(N)'

{txt}added macro:
              e(i3838) : "{res:}"

{com}.         estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}.         eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 [pweight=1/weight_comp] if order==2, robust
{txt}(sum of wgt is 1.40201240048e-06)
{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,937
                                                {txt}F(16, 1920)       =  {res}    41.23
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2304
                                                {txt}Root MSE          =    {res} .33409

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0237103{col 26}{space 2} .0287431{col 37}{space 1}    0.82{col 46}{space 3}0.410{col 54}{space 4}-.0326606{col 67}{space 3} .0800812
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0629398{col 26}{space 2} .0304629{col 37}{space 1}    2.07{col 46}{space 3}0.039{col 54}{space 4} .0031959{col 67}{space 3} .1226836
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0229904{col 26}{space 2} .0294917{col 37}{space 1}    0.78{col 46}{space 3}0.436{col 54}{space 4}-.0348487{col 67}{space 3} .0808294
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0001764{col 26}{space 2} .0247524{col 37}{space 1}   -0.01{col 46}{space 3}0.994{col 54}{space 4}-.0487209{col 67}{space 3} .0483681
{txt}{space 6}female {c |}{col 14}{res}{space 2}  .022801{col 26}{space 2} .0189067{col 37}{space 1}    1.21{col 46}{space 3}0.228{col 54}{space 4} -.014279{col 67}{space 3} .0598809
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0014125{col 26}{space 2} .0009017{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-.0031809{col 67}{space 3} .0003558
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0230149{col 26}{space 2} .0119826{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-.0465152{col 67}{space 3} .0004854
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0017287{col 26}{space 2} .0255909{col 37}{space 1}    0.07{col 46}{space 3}0.946{col 54}{space 4}  -.04846{col 67}{space 3} .0519175
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0092632{col 26}{space 2} .0040033{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4}-.0171146{col 67}{space 3}-.0014119
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3454502{col 26}{space 2} .0204518{col 37}{space 1}   16.89{col 46}{space 3}0.000{col 54}{space 4} .3053402{col 67}{space 3} .3855603
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0407523{col 26}{space 2} .0515515{col 37}{space 1}   -0.79{col 46}{space 3}0.429{col 54}{space 4}-.1418552{col 67}{space 3} .0603506
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0141834{col 26}{space 2} .0555223{col 37}{space 1}   -0.26{col 46}{space 3}0.798{col 54}{space 4}-.1230738{col 67}{space 3}  .094707
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0214814{col 26}{space 2} .0557097{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4}-.0877764{col 67}{space 3} .1307393
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0164112{col 26}{space 2} .0490647{col 37}{space 1}   -0.33{col 46}{space 3}0.738{col 54}{space 4}-.1126369{col 67}{space 3} .0798145
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0321074{col 26}{space 2} .0524133{col 37}{space 1}   -0.61{col 46}{space 3}0.540{col 54}{space 4}-.1349005{col 67}{space 3} .0706856
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0553272{col 26}{space 2} .0578509{col 37}{space 1}    0.96{col 46}{space 3}0.339{col 54}{space 4}-.0581301{col 67}{space 3} .1687844
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3056081{col 26}{space 2} .0864741{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .1360151{col 67}{space 3} .4752011
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}.         estadd local i`e(N)'

{txt}added macro:
              e(i1937) : "{res:}"

{com}.         estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}.         eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 [pweight=1/weight_comp] if order==1, robust
{txt}(sum of wgt is 1.34238888319e-06)
{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,901
                                                {txt}F(16, 1884)       =  {res}    18.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1661
                                                {txt}Root MSE          =    {res} .34767

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} -.067653{col 26}{space 2} .0342177{col 37}{space 1}   -1.98{col 46}{space 3}0.048{col 54}{space 4}-.1347616{col 67}{space 3}-.0005444
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0163033{col 26}{space 2} .0317634{col 37}{space 1}   -0.51{col 46}{space 3}0.608{col 54}{space 4}-.0785984{col 67}{space 3} .0459919
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0658416{col 26}{space 2} .0396019{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4}-.1435098{col 67}{space 3} .0118266
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0026216{col 26}{space 2} .0340551{col 37}{space 1}   -0.08{col 46}{space 3}0.939{col 54}{space 4}-.0694112{col 67}{space 3}  .064168
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0051313{col 26}{space 2} .0223596{col 37}{space 1}    0.23{col 46}{space 3}0.819{col 54}{space 4}-.0387208{col 67}{space 3} .0489834
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0032676{col 26}{space 2} .0012082{col 37}{space 1}   -2.70{col 46}{space 3}0.007{col 54}{space 4} -.005637{col 67}{space 3}-.0008981
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .007065{col 26}{space 2} .0129061{col 37}{space 1}    0.55{col 46}{space 3}0.584{col 54}{space 4}-.0182467{col 67}{space 3} .0323767
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0129989{col 26}{space 2} .0295795{col 37}{space 1}    0.44{col 46}{space 3}0.660{col 54}{space 4}-.0450131{col 67}{space 3} .0710108
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0020291{col 26}{space 2} .0046889{col 37}{space 1}   -0.43{col 46}{space 3}0.665{col 54}{space 4}-.0112251{col 67}{space 3} .0071668
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2909911{col 26}{space 2} .0245098{col 37}{space 1}   11.87{col 46}{space 3}0.000{col 54}{space 4}  .242922{col 67}{space 3} .3390603
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}  .000778{col 26}{space 2} .0493089{col 37}{space 1}    0.02{col 46}{space 3}0.987{col 54}{space 4}-.0959277{col 67}{space 3} .0974838
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0114712{col 26}{space 2} .0498461{col 37}{space 1}    0.23{col 46}{space 3}0.818{col 54}{space 4}-.0862881{col 67}{space 3} .1092306
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0258286{col 26}{space 2} .0436087{col 37}{space 1}   -0.59{col 46}{space 3}0.554{col 54}{space 4} -.111355{col 67}{space 3} .0596977
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}  .118907{col 26}{space 2} .0533259{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0143231{col 67}{space 3} .2234909
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0085953{col 26}{space 2}  .040661{col 37}{space 1}    0.21{col 46}{space 3}0.833{col 54}{space 4}-.0711499{col 67}{space 3} .0883406
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0957678{col 26}{space 2} .0515833{col 37}{space 1}    1.86{col 46}{space 3}0.064{col 54}{space 4}-.0053987{col 67}{space 3} .1969343
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2257325{col 26}{space 2} .0865218{col 37}{space 1}    2.61{col 46}{space 3}0.009{col 54}{space 4} .0560438{col 67}{space 3} .3954212
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}.         estadd local i`e(N)'

{txt}added macro:
              e(i1901) : "{res:}"

{com}.         estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}.         esttab using "`drive'/ATEweights.tex", replace ///
>                 b(2) se(2) nomtitles label ///
>                 booktabs ///
>                 star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>                 longtable ///
>                 s(sample i, label("Sample" "Observations")) ///
>                 title("Average Treatment Effects on Approval for Erdoğan with Post-Stratification Weights \label{c -(}tab:weights{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/ATEweights.tex"'})

{com}. restore
{txt}
{com}. 
. 
. 
. ************************************************************
. *** Table I4: Average Treatment Effects on Various Outcomes
. ************************************************************
. est clear
{res}{txt}
{com}. eststo: reg o2_std t2-t5 female age education govt_emp income islam r2-r8, robust       
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,731
                                                {txt}F(16, 3714)       =  {res}    37.48
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0691
                                                {txt}Root MSE          =    {res} .41718

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o2_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0154271{col 26}{space 2} .0218961{col 37}{space 1}   -0.70{col 46}{space 3}0.481{col 54}{space 4}-.0583566{col 67}{space 3} .0275024
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} -.011916{col 26}{space 2} .0216372{col 37}{space 1}   -0.55{col 46}{space 3}0.582{col 54}{space 4} -.054338{col 67}{space 3}  .030506
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0161252{col 26}{space 2} .0215707{col 37}{space 1}   -0.75{col 46}{space 3}0.455{col 54}{space 4}-.0584167{col 67}{space 3} .0261663
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0150456{col 26}{space 2} .0216718{col 37}{space 1}   -0.69{col 46}{space 3}0.488{col 54}{space 4}-.0575354{col 67}{space 3} .0274443
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0164562{col 26}{space 2} .0141123{col 37}{space 1}    1.17{col 46}{space 3}0.244{col 54}{space 4}-.0112126{col 67}{space 3} .0441249
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0023887{col 26}{space 2} .0006013{col 37}{space 1}   -3.97{col 46}{space 3}0.000{col 54}{space 4}-.0035677{col 67}{space 3}-.0012097
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0292108{col 26}{space 2} .0066075{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-.0421655{col 67}{space 3}-.0162562
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0316322{col 26}{space 2} .0186389{col 37}{space 1}   -1.70{col 46}{space 3}0.090{col 54}{space 4}-.0681757{col 67}{space 3} .0049113
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0035981{col 26}{space 2} .0027018{col 37}{space 1}   -1.33{col 46}{space 3}0.183{col 54}{space 4}-.0088953{col 67}{space 3}  .001699
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3125401{col 26}{space 2} .0158261{col 37}{space 1}   19.75{col 46}{space 3}0.000{col 54}{space 4} .2815113{col 67}{space 3} .3435688
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1269911{col 26}{space 2} .0339493{col 37}{space 1}   -3.74{col 46}{space 3}0.000{col 54}{space 4}-.1935521{col 67}{space 3}-.0604301
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0126218{col 26}{space 2} .0377256{col 37}{space 1}   -0.33{col 46}{space 3}0.738{col 54}{space 4}-.0865868{col 67}{space 3} .0613432
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0071162{col 26}{space 2}  .035028{col 37}{space 1}   -0.20{col 46}{space 3}0.839{col 54}{space 4}-.0757921{col 67}{space 3} .0615597
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0524887{col 26}{space 2} .0315786{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4}-.1144018{col 67}{space 3} .0094243
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0455809{col 26}{space 2} .0355673{col 37}{space 1}   -1.28{col 46}{space 3}0.200{col 54}{space 4}-.1153143{col 67}{space 3} .0241525
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} -.014357{col 26}{space 2} .0387876{col 37}{space 1}   -0.37{col 46}{space 3}0.711{col 54}{space 4}-.0904041{col 67}{space 3} .0616902
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4181074{col 26}{space 2} .0514873{col 37}{space 1}    8.12{col 46}{space 3}0.000{col 54}{space 4} .3171614{col 67}{space 3} .5190535
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3731}"

{com}. estadd local outcome "Erdoğan vote intention", replace

{txt}added macro:
            e(outcome) : "{res:Erdoğan vote intention}"

{com}. eststo: reg akp t2-t5 female age education govt_emp income islam r2-r8, robust  
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,129
                                                {txt}F(16, 3112)       =  {res}    27.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0599
                                                {txt}Root MSE          =    {res} .45385

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}         akp{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0086501{col 26}{space 2} .0258093{col 37}{space 1}   -0.34{col 46}{space 3}0.738{col 54}{space 4} -.059255{col 67}{space 3} .0419548
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0223763{col 26}{space 2} .0253405{col 37}{space 1}   -0.88{col 46}{space 3}0.377{col 54}{space 4}-.0720621{col 67}{space 3} .0273094
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0170701{col 26}{space 2} .0259347{col 37}{space 1}   -0.66{col 46}{space 3}0.510{col 54}{space 4}-.0679209{col 67}{space 3} .0337807
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0173189{col 26}{space 2} .0254993{col 37}{space 1}   -0.68{col 46}{space 3}0.497{col 54}{space 4} -.067316{col 67}{space 3} .0326783
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0359929{col 26}{space 2} .0167946{col 37}{space 1}    2.14{col 46}{space 3}0.032{col 54}{space 4} .0030633{col 67}{space 3} .0689224
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0032253{col 26}{space 2}   .00068{col 37}{space 1}   -4.74{col 46}{space 3}0.000{col 54}{space 4}-.0045585{col 67}{space 3}-.0018921
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0300296{col 26}{space 2} .0079808{col 37}{space 1}   -3.76{col 46}{space 3}0.000{col 54}{space 4}-.0456777{col 67}{space 3}-.0143815
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0288578{col 26}{space 2} .0219616{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4}-.0719184{col 67}{space 3} .0142028
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0050839{col 26}{space 2} .0032345{col 37}{space 1}   -1.57{col 46}{space 3}0.116{col 54}{space 4}-.0114258{col 67}{space 3} .0012581
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2680421{col 26}{space 2} .0167655{col 37}{space 1}   15.99{col 46}{space 3}0.000{col 54}{space 4} .2351696{col 67}{space 3} .3009147
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0933082{col 26}{space 2} .0418641{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-.1753923{col 67}{space 3}-.0112241
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0428387{col 26}{space 2} .0474304{col 37}{space 1}    0.90{col 46}{space 3}0.366{col 54}{space 4}-.0501594{col 67}{space 3} .1358367
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0520453{col 26}{space 2} .0431984{col 37}{space 1}    1.20{col 46}{space 3}0.228{col 54}{space 4} -.032655{col 67}{space 3} .1367456
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0226325{col 26}{space 2} .0395982{col 37}{space 1}   -0.57{col 46}{space 3}0.568{col 54}{space 4}-.1002738{col 67}{space 3} .0550088
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0163959{col 26}{space 2} .0442836{col 37}{space 1}   -0.37{col 46}{space 3}0.711{col 54}{space 4}-.1032239{col 67}{space 3} .0704322
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0637553{col 26}{space 2} .0491479{col 37}{space 1}    1.30{col 46}{space 3}0.195{col 54}{space 4}-.0326102{col 67}{space 3} .1601208
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3802624{col 26}{space 2} .0613583{col 37}{space 1}    6.20{col 46}{space 3}0.000{col 54}{space 4} .2599555{col 67}{space 3} .5005693
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3129}"

{com}. estadd local outcome "AKP vote intention", replace

{txt}added macro:
            e(outcome) : "{res:AKP vote intention}"

{com}. eststo: reg o6_std t2-t5 female age education govt_emp income islam r2-r8, robust       
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,670
                                                {txt}F(16, 3653)       =  {res}    19.53
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0538
                                                {txt}Root MSE          =    {res} .35596

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o6_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}  .007403{col 26}{space 2}  .018845{col 37}{space 1}    0.39{col 46}{space 3}0.694{col 54}{space 4}-.0295447{col 67}{space 3} .0443507
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0168809{col 26}{space 2} .0185546{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4}-.0532593{col 67}{space 3} .0194975
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0122009{col 26}{space 2} .0188257{col 37}{space 1}   -0.65{col 46}{space 3}0.517{col 54}{space 4}-.0491108{col 67}{space 3}  .024709
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0127576{col 26}{space 2} .0184032{col 37}{space 1}   -0.69{col 46}{space 3}0.488{col 54}{space 4}-.0488393{col 67}{space 3}  .023324
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0465112{col 26}{space 2} .0120835{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4}   .02282{col 67}{space 3} .0702023
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0028842{col 26}{space 2} .0004874{col 37}{space 1}   -5.92{col 46}{space 3}0.000{col 54}{space 4}-.0038398{col 67}{space 3}-.0019287
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0269932{col 26}{space 2} .0058593{col 37}{space 1}   -4.61{col 46}{space 3}0.000{col 54}{space 4} -.038481{col 67}{space 3}-.0155053
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0621336{col 26}{space 2} .0158006{col 37}{space 1}   -3.93{col 46}{space 3}0.000{col 54}{space 4}-.0931125{col 67}{space 3}-.0311547
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0085338{col 26}{space 2} .0023576{col 37}{space 1}   -3.62{col 46}{space 3}0.000{col 54}{space 4}-.0131562{col 67}{space 3}-.0039114
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1587783{col 26}{space 2} .0142319{col 37}{space 1}   11.16{col 46}{space 3}0.000{col 54}{space 4} .1308751{col 67}{space 3} .1866816
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0430635{col 26}{space 2} .0303383{col 37}{space 1}   -1.42{col 46}{space 3}0.156{col 54}{space 4}-.1025451{col 67}{space 3} .0164181
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0053711{col 26}{space 2} .0336836{col 37}{space 1}    0.16{col 46}{space 3}0.873{col 54}{space 4}-.0606694{col 67}{space 3} .0714117
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0556449{col 26}{space 2} .0318265{col 37}{space 1}    1.75{col 46}{space 3}0.080{col 54}{space 4}-.0067546{col 67}{space 3} .1180444
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0067953{col 26}{space 2} .0287514{col 37}{space 1}   -0.24{col 46}{space 3}0.813{col 54}{space 4}-.0631656{col 67}{space 3}  .049575
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0033986{col 26}{space 2} .0321917{col 37}{space 1}   -0.11{col 46}{space 3}0.916{col 54}{space 4} -.066514{col 67}{space 3} .0597169
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0434775{col 26}{space 2}  .035496{col 37}{space 1}    1.22{col 46}{space 3}0.221{col 54}{space 4}-.0261164{col 67}{space 3} .1130714
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3906093{col 26}{space 2} .0469578{col 37}{space 1}    8.32{col 46}{space 3}0.000{col 54}{space 4} .2985432{col 67}{space 3} .4826754
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3670}"

{com}. estadd local outcome "Erdoğan volunteer intention", replace

{txt}added macro:
            e(outcome) : "{res:Erdoğan volunteer intention}"

{com}. eststo: reg o8_std t2-t5 female age education govt_emp income islam r2-r8, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,778
                                                {txt}F(16, 3761)       =  {res}    20.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0534
                                                {txt}Root MSE          =    {res} .38593

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o8_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0297847{col 26}{space 2} .0200484{col 37}{space 1}   -1.49{col 46}{space 3}0.137{col 54}{space 4}-.0690915{col 67}{space 3} .0095222
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0288852{col 26}{space 2} .0199353{col 37}{space 1}   -1.45{col 46}{space 3}0.147{col 54}{space 4}-.0679702{col 67}{space 3} .0101999
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0287295{col 26}{space 2}  .019894{col 37}{space 1}   -1.44{col 46}{space 3}0.149{col 54}{space 4}-.0677337{col 67}{space 3} .0102746
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0103292{col 26}{space 2} .0199436{col 37}{space 1}   -0.52{col 46}{space 3}0.605{col 54}{space 4}-.0494305{col 67}{space 3} .0287721
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0026902{col 26}{space 2} .0130061{col 37}{space 1}   -0.21{col 46}{space 3}0.836{col 54}{space 4}-.0281899{col 67}{space 3} .0228095
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0010852{col 26}{space 2} .0005548{col 37}{space 1}   -1.96{col 46}{space 3}0.051{col 54}{space 4}-.0021729{col 67}{space 3} 2.52e-06
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0269838{col 26}{space 2} .0060848{col 37}{space 1}   -4.43{col 46}{space 3}0.000{col 54}{space 4}-.0389137{col 67}{space 3}-.0150539
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0151548{col 26}{space 2}  .017124{col 37}{space 1}   -0.89{col 46}{space 3}0.376{col 54}{space 4} -.048728{col 67}{space 3} .0184184
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0062012{col 26}{space 2} .0025151{col 37}{space 1}   -2.47{col 46}{space 3}0.014{col 54}{space 4}-.0111322{col 67}{space 3}-.0012702
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2450642{col 26}{space 2}  .017182{col 37}{space 1}   14.26{col 46}{space 3}0.000{col 54}{space 4} .2113772{col 67}{space 3} .2787511
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1020639{col 26}{space 2} .0314348{col 37}{space 1}   -3.25{col 46}{space 3}0.001{col 54}{space 4}-.1636948{col 67}{space 3}-.0404329
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0119486{col 26}{space 2} .0347618{col 37}{space 1}   -0.34{col 46}{space 3}0.731{col 54}{space 4}-.0801024{col 67}{space 3} .0562051
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0031381{col 26}{space 2} .0325789{col 37}{space 1}    0.10{col 46}{space 3}0.923{col 54}{space 4}-.0607359{col 67}{space 3} .0670122
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0436546{col 26}{space 2} .0292601{col 37}{space 1}   -1.49{col 46}{space 3}0.136{col 54}{space 4}-.1010218{col 67}{space 3} .0137126
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0368873{col 26}{space 2} .0329236{col 37}{space 1}   -1.12{col 46}{space 3}0.263{col 54}{space 4}-.1014372{col 67}{space 3} .0276626
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0347757{col 26}{space 2} .0359473{col 37}{space 1}   -0.97{col 46}{space 3}0.333{col 54}{space 4}-.1052539{col 67}{space 3} .0357025
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4228417{col 26}{space 2} .0485416{col 37}{space 1}    8.71{col 46}{space 3}0.000{col 54}{space 4} .3276713{col 67}{space 3}  .518012
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3778}"

{com}. estadd local outcome "Government earthquake response", replace

{txt}added macro:
            e(outcome) : "{res:Government earthquake response}"

{com}. eststo: kict ls dv t2-t5 female age education govt_emp income islam r2-r8, nn(3) est(linear) cond(dummy) vce(robust)
{txt}{p 0 2 6}note: {bf:instrument r8} omitted because of collinearity.{p_end}
{res}
{txt}Step {res}1
{txt}Iteration 0:{space 2}GMM criterion Q(b) = {res: .47310432}  (not concave)
Iteration 1:{space 2}GMM criterion Q(b) = {res: .07323237}  (not concave)
Iteration 2:{space 2}GMM criterion Q(b) = {res: .02104698}  (not concave)
Iteration 3:{space 2}GMM criterion Q(b) = {res: .01237022}  (not concave)
Iteration 4:{space 2}GMM criterion Q(b) = {res: .00748448}  (not concave)
Iteration 5:{space 2}GMM criterion Q(b) = {res: .00568058}  (not concave)
Iteration 6:{space 2}GMM criterion Q(b) = {res: .00443201}  (not concave)
Iteration 7:{space 2}GMM criterion Q(b) = {res:   .003608}  (not concave)
Iteration 8:{space 2}GMM criterion Q(b) = {res: .00293162}  (not concave)
Iteration 9:{space 2}GMM criterion Q(b) = {res: .00257894}  (not concave)
Iteration 10:{space 1}GMM criterion Q(b) = {res: .00233265}  (not concave)
Iteration 11:{space 1}GMM criterion Q(b) = {res: .00196243}  (not concave)
Iteration 12:{space 1}GMM criterion Q(b) = {res: .00171616}  (not concave)
Iteration 13:{space 1}GMM criterion Q(b) = {res: .00159325}  (not concave)
Iteration 14:{space 1}GMM criterion Q(b) = {res: .00148557}  (not concave)
Iteration 15:{space 1}GMM criterion Q(b) = {res: .00133718}  (not concave)
Iteration 16:{space 1}GMM criterion Q(b) = {res: .00120648}  (not concave)
Iteration 17:{space 1}GMM criterion Q(b) = {res: .00096039}  (not concave)
Iteration 18:{space 1}GMM criterion Q(b) = {res: .00087564}  (not concave)
Iteration 19:{space 1}GMM criterion Q(b) = {res: .00047404}  (not concave)
Iteration 20:{space 1}GMM criterion Q(b) = {res: .00037505}  (not concave)
Iteration 21:{space 1}GMM criterion Q(b) = {res: .00035477}  (not concave)
Iteration 22:{space 1}GMM criterion Q(b) = {res: .00033155}  (not concave)
Iteration 23:{space 1}GMM criterion Q(b) = {res:  .0003135}  (not concave)
Iteration 24:{space 1}GMM criterion Q(b) = {res: .00029729}  (not concave)
Iteration 25:{space 1}GMM criterion Q(b) = {res: .00028335}  (not concave)
Iteration 26:{space 1}GMM criterion Q(b) = {res: .00027066}  (not concave)
Iteration 27:{space 1}GMM criterion Q(b) = {res: .00025902}  (not concave)
Iteration 28:{space 1}GMM criterion Q(b) = {res: .00024841}  (not concave)
Iteration 29:{space 1}GMM criterion Q(b) = {res: .00023867}  (not concave)
Iteration 30:{space 1}GMM criterion Q(b) = {res: .00022965}  (not concave)
Iteration 31:{space 1}GMM criterion Q(b) = {res: .00022126}  (not concave)
Iteration 32:{space 1}GMM criterion Q(b) = {res: .00021343}  (not concave)
Iteration 33:{space 1}GMM criterion Q(b) = {res: .00020609}  (not concave)
Iteration 34:{space 1}GMM criterion Q(b) = {res: .00019918}  (not concave)
Iteration 35:{space 1}GMM criterion Q(b) = {res: .00019265}  (not concave)
Iteration 36:{space 1}GMM criterion Q(b) = {res: .00018645}  (not concave)
Iteration 37:{space 1}GMM criterion Q(b) = {res: .00018057}  (not concave)
Iteration 38:{space 1}GMM criterion Q(b) = {res: .00017495}  (not concave)
Iteration 39:{space 1}GMM criterion Q(b) = {res: .00016958}  (not concave)
Iteration 40:{space 1}GMM criterion Q(b) = {res: .00016444}  (not concave)
Iteration 41:{space 1}GMM criterion Q(b) = {res: .00015951}  (not concave)
Iteration 42:{space 1}GMM criterion Q(b) = {res: .00015477}  (not concave)
Iteration 43:{space 1}GMM criterion Q(b) = {res:  .0001502}  (not concave)
Iteration 44:{space 1}GMM criterion Q(b) = {res:  .0001458}  (not concave)
Iteration 45:{space 1}GMM criterion Q(b) = {res: .00014155}  (not concave)
Iteration 46:{space 1}GMM criterion Q(b) = {res: .00013745}  (not concave)
Iteration 47:{space 1}GMM criterion Q(b) = {res: .00013349}  (not concave)
Iteration 48:{space 1}GMM criterion Q(b) = {res: .00012965}  (not concave)
Iteration 49:{space 1}GMM criterion Q(b) = {res: .00012594}  (not concave)
Iteration 50:{space 1}GMM criterion Q(b) = {res: .00012234}  (not concave)
Iteration 51:{space 1}GMM criterion Q(b) = {res: .00011886}  (not concave)
Iteration 52:{space 1}GMM criterion Q(b) = {res: .00011548}  (not concave)
Iteration 53:{space 1}GMM criterion Q(b) = {res: .00011221}  (not concave)
Iteration 54:{space 1}GMM criterion Q(b) = {res: .00010903}  (not concave)
Iteration 55:{space 1}GMM criterion Q(b) = {res: .00010595}  (not concave)
Iteration 56:{space 1}GMM criterion Q(b) = {res: .00010296}  (not concave)
Iteration 57:{space 1}GMM criterion Q(b) = {res: .00010005}  (not concave)
Iteration 58:{space 1}GMM criterion Q(b) = {res: .00009723}  (not concave)
Iteration 59:{space 1}GMM criterion Q(b) = {res: .00009449}  (not concave)
Iteration 60:{space 1}GMM criterion Q(b) = {res: .00009183}  (not concave)
Iteration 61:{space 1}GMM criterion Q(b) = {res: .00008925}  (not concave)
Iteration 62:{space 1}GMM criterion Q(b) = {res: .00008674}  (not concave)
Iteration 63:{space 1}GMM criterion Q(b) = {res: .00008431}  (not concave)
Iteration 64:{space 1}GMM criterion Q(b) = {res: .00008194}  (not concave)
Iteration 65:{space 1}GMM criterion Q(b) = {res: .00007964}  (not concave)
Iteration 66:{space 1}GMM criterion Q(b) = {res: .00007741}  (not concave)
Iteration 67:{space 1}GMM criterion Q(b) = {res: .00007523}  (not concave)
Iteration 68:{space 1}GMM criterion Q(b) = {res: .00007312}  (not concave)
Iteration 69:{space 1}GMM criterion Q(b) = {res: .00007107}  (not concave)
Iteration 70:{space 1}GMM criterion Q(b) = {res: .00006908}  (not concave)
Iteration 71:{space 1}GMM criterion Q(b) = {res: .00006715}  (not concave)
Iteration 72:{space 1}GMM criterion Q(b) = {res: .00006526}  (not concave)
Iteration 73:{space 1}GMM criterion Q(b) = {res: .00006344}  (not concave)
Iteration 74:{space 1}GMM criterion Q(b) = {res: .00006166}  (not concave)
Iteration 75:{space 1}GMM criterion Q(b) = {res: .00005993}  (not concave)
Iteration 76:{space 1}GMM criterion Q(b) = {res: .00005825}  (not concave)
Iteration 77:{space 1}GMM criterion Q(b) = {res: .00005662}  (not concave)
Iteration 78:{space 1}GMM criterion Q(b) = {res: .00005504}  (not concave)
Iteration 79:{space 1}GMM criterion Q(b) = {res:  .0000535}  (not concave)
Iteration 80:{space 1}GMM criterion Q(b) = {res:   .000052}  (not concave)
Iteration 81:{space 1}GMM criterion Q(b) = {res: .00005054}  (not concave)
Iteration 82:{space 1}GMM criterion Q(b) = {res: .00004913}  (not concave)
Iteration 83:{space 1}GMM criterion Q(b) = {res: .00004775}  (not concave)
Iteration 84:{space 1}GMM criterion Q(b) = {res: .00004642}  (not concave)
Iteration 85:{space 1}GMM criterion Q(b) = {res: .00004512}  (not concave)
Iteration 86:{space 1}GMM criterion Q(b) = {res: .00004385}  (not concave)
Iteration 87:{space 1}GMM criterion Q(b) = {res: .00004263}  (not concave)
Iteration 88:{space 1}GMM criterion Q(b) = {res: .00004143}  (not concave)
Iteration 89:{space 1}GMM criterion Q(b) = {res: .00004027}  (not concave)
Iteration 90:{space 1}GMM criterion Q(b) = {res: .00003915}  (not concave)
Iteration 91:{space 1}GMM criterion Q(b) = {res: .00003805}  (not concave)
Iteration 92:{space 1}GMM criterion Q(b) = {res: .00003699}  (not concave)
Iteration 93:{space 1}GMM criterion Q(b) = {res: .00003595}  (not concave)
Iteration 94:{space 1}GMM criterion Q(b) = {res: .00003495}  (not concave)
Iteration 95:{space 1}GMM criterion Q(b) = {res: .00003397}  (not concave)
Iteration 96:{space 1}GMM criterion Q(b) = {res: .00003302}  (not concave)
Iteration 97:{space 1}GMM criterion Q(b) = {res: .00003209}  (not concave)
Iteration 98:{space 1}GMM criterion Q(b) = {res:  .0000312}  (not concave)
Iteration 99:{space 1}GMM criterion Q(b) = {res: .00003032}  (not concave)
Iteration 100:{space 1}GMM criterion Q(b) = {res: .00002947}  (not concave)
Iteration 101:{space 1}GMM criterion Q(b) = {res: .00002865}  (not concave)
Iteration 102:{space 1}GMM criterion Q(b) = {res: .00002785}  (not concave)
Iteration 103:{space 1}GMM criterion Q(b) = {res: .00002707}  (not concave)
Iteration 104:{space 1}GMM criterion Q(b) = {res: .00002631}  (not concave)
Iteration 105:{space 1}GMM criterion Q(b) = {res: .00002558}  (not concave)
Iteration 106:{space 1}GMM criterion Q(b) = {res: .00002486}  (not concave)
Iteration 107:{space 1}GMM criterion Q(b) = {res: .00002416}  (not concave)
Iteration 108:{space 1}GMM criterion Q(b) = {res: .00002349}  (not concave)
Iteration 109:{space 1}GMM criterion Q(b) = {res: .00002283}  (not concave)
Iteration 110:{space 1}GMM criterion Q(b) = {res: .00002219}  (not concave)
Iteration 111:{space 1}GMM criterion Q(b) = {res: .00002157}  (not concave)
Iteration 112:{space 1}GMM criterion Q(b) = {res: .00002097}  (not concave)
Iteration 113:{space 1}GMM criterion Q(b) = {res: .00002038}  (not concave)
Iteration 114:{space 1}GMM criterion Q(b) = {res: .00001981}  (not concave)
Iteration 115:{space 1}GMM criterion Q(b) = {res: .00001926}  (not concave)
Iteration 116:{space 1}GMM criterion Q(b) = {res: .00001872}  (not concave)
Iteration 117:{space 1}GMM criterion Q(b) = {res: .00001819}  (not concave)
Iteration 118:{space 1}GMM criterion Q(b) = {res: .00001769}  (not concave)
Iteration 119:{space 1}GMM criterion Q(b) = {res: .00001719}  (not concave)
Iteration 120:{space 1}GMM criterion Q(b) = {res: .00001671}  (not concave)
Iteration 121:{space 1}GMM criterion Q(b) = {res: .00001624}  (not concave)
Iteration 122:{space 1}GMM criterion Q(b) = {res: .00001579}  (not concave)
Iteration 123:{space 1}GMM criterion Q(b) = {res: .00001535}  (not concave)
Iteration 124:{space 1}GMM criterion Q(b) = {res: .00001492}  (not concave)
Iteration 125:{space 1}GMM criterion Q(b) = {res:  .0000145}  (not concave)
Iteration 126:{space 1}GMM criterion Q(b) = {res: .00001409}  (not concave)
Iteration 127:{space 1}GMM criterion Q(b) = {res:  .0000137}  (not concave)
Iteration 128:{space 1}GMM criterion Q(b) = {res: .00001332}  (not concave)
Iteration 129:{space 1}GMM criterion Q(b) = {res: .00001294}  (not concave)
Iteration 130:{space 1}GMM criterion Q(b) = {res: .00001258}  (not concave)
Iteration 131:{space 1}GMM criterion Q(b) = {res: .00001223}  (not concave)
Iteration 132:{space 1}GMM criterion Q(b) = {res: .00001189}  (not concave)
Iteration 133:{space 1}GMM criterion Q(b) = {res: .00001156}  (not concave)
Iteration 134:{space 1}GMM criterion Q(b) = {res: .00001123}  (not concave)
Iteration 135:{space 1}GMM criterion Q(b) = {res: .00001092}  (not concave)
Iteration 136:{space 1}GMM criterion Q(b) = {res: .00001061}  (not concave)
Iteration 137:{space 1}GMM criterion Q(b) = {res: .00001032}  (not concave)
Iteration 138:{space 1}GMM criterion Q(b) = {res: .00001003}  (not concave)
Iteration 139:{space 1}GMM criterion Q(b) = {res: 9.747e-06}  (not concave)
Iteration 140:{space 1}GMM criterion Q(b) = {res: 9.474e-06}  (not concave)
Iteration 141:{space 1}GMM criterion Q(b) = {res: 9.209e-06}  (not concave)
Iteration 142:{space 1}GMM criterion Q(b) = {res: 8.951e-06}  (not concave)
Iteration 143:{space 1}GMM criterion Q(b) = {res: 8.701e-06}  (not concave)
Iteration 144:{space 1}GMM criterion Q(b) = {res: 8.458e-06}  (not concave)
Iteration 145:{space 1}GMM criterion Q(b) = {res: 8.221e-06}  (not concave)
Iteration 146:{space 1}GMM criterion Q(b) = {res: 7.991e-06}  (not concave)
Iteration 147:{space 1}GMM criterion Q(b) = {res: 7.767e-06}  (not concave)
Iteration 148:{space 1}GMM criterion Q(b) = {res: 7.550e-06}  (not concave)
Iteration 149:{space 1}GMM criterion Q(b) = {res: 7.339e-06}  (not concave)
Iteration 150:{space 1}GMM criterion Q(b) = {res: 7.134e-06}  (not concave)
Iteration 151:{space 1}GMM criterion Q(b) = {res: 6.934e-06}  (not concave)
Iteration 152:{space 1}GMM criterion Q(b) = {res: 6.740e-06}  (not concave)
Iteration 153:{space 1}GMM criterion Q(b) = {res: 6.552e-06}  (not concave)
Iteration 154:{space 1}GMM criterion Q(b) = {res: 6.368e-06}  (not concave)
Iteration 155:{space 1}GMM criterion Q(b) = {res: 6.190e-06}  (not concave)
Iteration 156:{space 1}GMM criterion Q(b) = {res: 6.017e-06}  (not concave)
Iteration 157:{space 1}GMM criterion Q(b) = {res: 5.849e-06}  (not concave)
Iteration 158:{space 1}GMM criterion Q(b) = {res: 5.685e-06}  (not concave)
Iteration 159:{space 1}GMM criterion Q(b) = {res: 5.526e-06}  (not concave)
Iteration 160:{space 1}GMM criterion Q(b) = {res: 5.371e-06}  (not concave)
Iteration 161:{space 1}GMM criterion Q(b) = {res: 5.221e-06}  (not concave)
Iteration 162:{space 1}GMM criterion Q(b) = {res: 5.075e-06}  (not concave)
Iteration 163:{space 1}GMM criterion Q(b) = {res: 4.933e-06}  (not concave)
Iteration 164:{space 1}GMM criterion Q(b) = {res: 4.795e-06}  (not concave)
Iteration 165:{space 1}GMM criterion Q(b) = {res: 4.661e-06}  (not concave)
Iteration 166:{space 1}GMM criterion Q(b) = {res: 4.531e-06}  (not concave)
Iteration 167:{space 1}GMM criterion Q(b) = {res: 4.404e-06}  (not concave)
Iteration 168:{space 1}GMM criterion Q(b) = {res: 4.281e-06}  (not concave)
Iteration 169:{space 1}GMM criterion Q(b) = {res: 4.161e-06}  (not concave)
Iteration 170:{space 1}GMM criterion Q(b) = {res: 4.045e-06}  (not concave)
Iteration 171:{space 1}GMM criterion Q(b) = {res: 3.931e-06}  (not concave)
Iteration 172:{space 1}GMM criterion Q(b) = {res: 3.821e-06}  (not concave)
Iteration 173:{space 1}GMM criterion Q(b) = {res: 3.715e-06}  (not concave)
Iteration 174:{space 1}GMM criterion Q(b) = {res: 3.611e-06}  (not concave)
Iteration 175:{space 1}GMM criterion Q(b) = {res: 3.510e-06}  (not concave)
Iteration 176:{space 1}GMM criterion Q(b) = {res: 3.411e-06}  (not concave)
Iteration 177:{space 1}GMM criterion Q(b) = {res: 3.316e-06}  (not concave)
Iteration 178:{space 1}GMM criterion Q(b) = {res: 3.223e-06}  (not concave)
Iteration 179:{space 1}GMM criterion Q(b) = {res: 3.133e-06}  (not concave)
Iteration 180:{space 1}GMM criterion Q(b) = {res: 3.045e-06}  (not concave)
Iteration 181:{space 1}GMM criterion Q(b) = {res: 2.960e-06}  (not concave)
Iteration 182:{space 1}GMM criterion Q(b) = {res: 2.877e-06}  (not concave)
Iteration 183:{space 1}GMM criterion Q(b) = {res: 2.797e-06}  (not concave)
Iteration 184:{space 1}GMM criterion Q(b) = {res: 2.719e-06}  (not concave)
Iteration 185:{space 1}GMM criterion Q(b) = {res: 2.643e-06}  (not concave)
Iteration 186:{space 1}GMM criterion Q(b) = {res: 2.569e-06}  (not concave)
Iteration 187:{space 1}GMM criterion Q(b) = {res: 2.497e-06}  (not concave)
Iteration 188:{space 1}GMM criterion Q(b) = {res: 2.427e-06}  (not concave)
Iteration 189:{space 1}GMM criterion Q(b) = {res: 2.359e-06}  (not concave)
Iteration 190:{space 1}GMM criterion Q(b) = {res: 2.293e-06}  (not concave)
Iteration 191:{space 1}GMM criterion Q(b) = {res: 2.229e-06}  (not concave)
Iteration 192:{space 1}GMM criterion Q(b) = {res: 2.167e-06}  (not concave)
Iteration 193:{space 1}GMM criterion Q(b) = {res: 2.106e-06}  (not concave)
Iteration 194:{space 1}GMM criterion Q(b) = {res: 2.047e-06}  (not concave)
Iteration 195:{space 1}GMM criterion Q(b) = {res: 1.990e-06}  (not concave)
Iteration 196:{space 1}GMM criterion Q(b) = {res: 1.934e-06}  (not concave)
Iteration 197:{space 1}GMM criterion Q(b) = {res: 1.880e-06}  (not concave)
Iteration 198:{space 1}GMM criterion Q(b) = {res: 1.828e-06}  (not concave)
Iteration 199:{space 1}GMM criterion Q(b) = {res: 1.776e-06}  (not concave)
Iteration 200:{space 1}GMM criterion Q(b) = {res: 1.727e-06}  (not concave)
Iteration 201:{space 1}GMM criterion Q(b) = {res: 1.678e-06}  (not concave)
Iteration 202:{space 1}GMM criterion Q(b) = {res: 1.631e-06}  (not concave)
Iteration 203:{space 1}GMM criterion Q(b) = {res: 1.586e-06}  (not concave)
Iteration 204:{space 1}GMM criterion Q(b) = {res: 1.541e-06}  (not concave)
Iteration 205:{space 1}GMM criterion Q(b) = {res: 1.498e-06}  (not concave)
Iteration 206:{space 1}GMM criterion Q(b) = {res: 1.456e-06}  (not concave)
Iteration 207:{space 1}GMM criterion Q(b) = {res: 1.416e-06}  (not concave)
Iteration 208:{space 1}GMM criterion Q(b) = {res: 1.376e-06}  (not concave)
Iteration 209:{space 1}GMM criterion Q(b) = {res: 1.338e-06}  (not concave)
Iteration 210:{space 1}GMM criterion Q(b) = {res: 1.300e-06}  (not concave)
Iteration 211:{space 1}GMM criterion Q(b) = {res: 1.264e-06}  (not concave)
Iteration 212:{space 1}GMM criterion Q(b) = {res: 1.228e-06}  (not concave)
Iteration 213:{space 1}GMM criterion Q(b) = {res: 1.194e-06}  (not concave)
Iteration 214:{space 1}GMM criterion Q(b) = {res: 1.161e-06}  (not concave)
Iteration 215:{space 1}GMM criterion Q(b) = {res: 1.128e-06}  (not concave)
Iteration 216:{space 1}GMM criterion Q(b) = {res: 1.097e-06}  (not concave)
Iteration 217:{space 1}GMM criterion Q(b) = {res: 1.066e-06}  (not concave)
Iteration 218:{space 1}GMM criterion Q(b) = {res: 1.036e-06}  (not concave)
Iteration 219:{space 1}GMM criterion Q(b) = {res: 1.007e-06}  (not concave)
Iteration 220:{space 1}GMM criterion Q(b) = {res: 9.790e-07}  (not concave)
Iteration 221:{space 1}GMM criterion Q(b) = {res: 9.516e-07}  (not concave)
Iteration 222:{space 1}GMM criterion Q(b) = {res: 9.250e-07}  (not concave)
Iteration 223:{space 1}GMM criterion Q(b) = {res: 8.991e-07}  (not concave)
Iteration 224:{space 1}GMM criterion Q(b) = {res: 8.740e-07}  (not concave)
Iteration 225:{space 1}GMM criterion Q(b) = {res: 8.495e-07}  (not concave)
Iteration 226:{space 1}GMM criterion Q(b) = {res: 8.258e-07}  (not concave)
Iteration 227:{space 1}GMM criterion Q(b) = {res: 8.027e-07}  (not concave)
Iteration 228:{space 1}GMM criterion Q(b) = {res: 7.802e-07}  (not concave)
Iteration 229:{space 1}GMM criterion Q(b) = {res: 7.584e-07}  (not concave)
Iteration 230:{space 1}GMM criterion Q(b) = {res: 7.372e-07}  (not concave)
Iteration 231:{space 1}GMM criterion Q(b) = {res: 7.165e-07}  (not concave)
Iteration 232:{space 1}GMM criterion Q(b) = {res: 6.965e-07}  (not concave)
Iteration 233:{space 1}GMM criterion Q(b) = {res: 6.770e-07}  (not concave)
Iteration 234:{space 1}GMM criterion Q(b) = {res: 6.581e-07}  (not concave)
Iteration 235:{space 1}GMM criterion Q(b) = {res: 6.397e-07}  (not concave)
Iteration 236:{space 1}GMM criterion Q(b) = {res: 6.218e-07}  (not concave)
Iteration 237:{space 1}GMM criterion Q(b) = {res: 6.044e-07}  (not concave)
Iteration 238:{space 1}GMM criterion Q(b) = {res: 5.875e-07}  (not concave)
Iteration 239:{space 1}GMM criterion Q(b) = {res: 5.710e-07}  (not concave)
Iteration 240:{space 1}GMM criterion Q(b) = {res: 5.551e-07}  (not concave)
Iteration 241:{space 1}GMM criterion Q(b) = {res: 5.395e-07}  (not concave)
Iteration 242:{space 1}GMM criterion Q(b) = {res: 5.245e-07}  (not concave)
Iteration 243:{space 1}GMM criterion Q(b) = {res: 5.098e-07}  (not concave)
Iteration 244:{space 1}GMM criterion Q(b) = {res: 4.955e-07}  (not concave)
Iteration 245:{space 1}GMM criterion Q(b) = {res: 4.817e-07}  (not concave)
Iteration 246:{space 1}GMM criterion Q(b) = {res: 4.682e-07}  (not concave)
Iteration 247:{space 1}GMM criterion Q(b) = {res: 4.551e-07}  (not concave)
Iteration 248:{space 1}GMM criterion Q(b) = {res: 4.424e-07}  (not concave)
Iteration 249:{space 1}GMM criterion Q(b) = {res: 4.300e-07}  (not concave)
Iteration 250:{space 1}GMM criterion Q(b) = {res: 4.180e-07}  (not concave)
Iteration 251:{space 1}GMM criterion Q(b) = {res: 4.063e-07}  (not concave)
Iteration 252:{space 1}GMM criterion Q(b) = {res: 3.949e-07}  (not concave)
Iteration 253:{space 1}GMM criterion Q(b) = {res: 3.839e-07}  (not concave)
Iteration 254:{space 1}GMM criterion Q(b) = {res: 3.731e-07}  (not concave)
Iteration 255:{space 1}GMM criterion Q(b) = {res: 3.627e-07}  (not concave)
Iteration 256:{space 1}GMM criterion Q(b) = {res: 3.525e-07}  (not concave)
Iteration 257:{space 1}GMM criterion Q(b) = {res: 3.427e-07}  (not concave)
Iteration 258:{space 1}GMM criterion Q(b) = {res: 3.331e-07}  (not concave)
Iteration 259:{space 1}GMM criterion Q(b) = {res: 3.238e-07}  (not concave)
Iteration 260:{space 1}GMM criterion Q(b) = {res: 3.147e-07}  (not concave)
Iteration 261:{space 1}GMM criterion Q(b) = {res: 3.059e-07}  (not concave)
Iteration 262:{space 1}GMM criterion Q(b) = {res: 2.974e-07}  (not concave)
Iteration 263:{space 1}GMM criterion Q(b) = {res: 2.890e-07}  (not concave)
Iteration 264:{space 1}GMM criterion Q(b) = {res: 2.809e-07}  (not concave)
Iteration 265:{space 1}GMM criterion Q(b) = {res: 2.731e-07}  (not concave)
Iteration 266:{space 1}GMM criterion Q(b) = {res: 2.655e-07}  (not concave)
Iteration 267:{space 1}GMM criterion Q(b) = {res: 2.580e-07}  (not concave)
Iteration 268:{space 1}GMM criterion Q(b) = {res: 2.508e-07}  (not concave)
Iteration 269:{space 1}GMM criterion Q(b) = {res: 2.438e-07}  (not concave)
Iteration 270:{space 1}GMM criterion Q(b) = {res: 2.370e-07}  (not concave)
Iteration 271:{space 1}GMM criterion Q(b) = {res: 2.303e-07}  (not concave)
Iteration 272:{space 1}GMM criterion Q(b) = {res: 2.239e-07}  (not concave)
Iteration 273:{space 1}GMM criterion Q(b) = {res: 2.176e-07}  (not concave)
Iteration 274:{space 1}GMM criterion Q(b) = {res: 2.115e-07}  (not concave)
Iteration 275:{space 1}GMM criterion Q(b) = {res: 2.056e-07}  (not concave)
Iteration 276:{space 1}GMM criterion Q(b) = {res: 1.999e-07}  (not concave)
Iteration 277:{space 1}GMM criterion Q(b) = {res: 1.943e-07}  (not concave)
Iteration 278:{space 1}GMM criterion Q(b) = {res: 1.889e-07}  (not concave)
Iteration 279:{space 1}GMM criterion Q(b) = {res: 1.836e-07}  (not concave)
Iteration 280:{space 1}GMM criterion Q(b) = {res: 1.784e-07}  (not concave)
Iteration 281:{space 1}GMM criterion Q(b) = {res: 1.734e-07}  (not concave)
Iteration 282:{space 1}GMM criterion Q(b) = {res: 1.686e-07}  (not concave)
Iteration 283:{space 1}GMM criterion Q(b) = {res: 1.639e-07}  (not concave)
Iteration 284:{space 1}GMM criterion Q(b) = {res: 1.593e-07}  (not concave)
Iteration 285:{space 1}GMM criterion Q(b) = {res: 1.548e-07}  (not concave)
Iteration 286:{space 1}GMM criterion Q(b) = {res: 1.505e-07}  (not concave)
Iteration 287:{space 1}GMM criterion Q(b) = {res: 1.463e-07}  (not concave)
Iteration 288:{space 1}GMM criterion Q(b) = {res: 1.422e-07}  (not concave)
Iteration 289:{space 1}GMM criterion Q(b) = {res: 1.382e-07}  (not concave)
Iteration 290:{space 1}GMM criterion Q(b) = {res: 1.344e-07}  (not concave)
Iteration 291:{space 1}GMM criterion Q(b) = {res: 1.306e-07}  (not concave)
Iteration 292:{space 1}GMM criterion Q(b) = {res: 1.269e-07}  (not concave)
Iteration 293:{space 1}GMM criterion Q(b) = {res: 1.234e-07}  (not concave)
Iteration 294:{space 1}GMM criterion Q(b) = {res: 1.199e-07}  (not concave)
Iteration 295:{space 1}GMM criterion Q(b) = {res: 1.166e-07}  (not concave)
Iteration 296:{space 1}GMM criterion Q(b) = {res: 1.133e-07}  (not concave)
Iteration 297:{space 1}GMM criterion Q(b) = {res: 1.102e-07}  (not concave)
Iteration 298:{space 1}GMM criterion Q(b) = {res: 1.071e-07}  (not concave)
Iteration 299:{space 1}GMM criterion Q(b) = {res: 1.041e-07}  (not concave)
Iteration 300:{space 1}GMM criterion Q(b) = {res: 1.012e-07}  (not concave)
{err}convergence not achieved
{res}
{txt}GMM estimation 

{col 1}Number of parameters = {col 24}{res} 36
{txt}{col 1}Number of moments    = {col 24}{res} 34
{txt}{col 1}Initial weight matrix: {col 24}{res}Unadjusted{txt}{col 51}Number of obs{col 67}= {res}     3,562
{txt}{col 1}GMM weight matrix: {col 24}{res}Robust

{txt}Linear least squares estimator
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Delta        {txt}{c |}
{space 10}t2 {c |}{col 14}{res}{space 2} -.144167{col 26}{space 2} .0985401{col 37}{space 1}   -1.46{col 46}{space 3}0.143{col 54}{space 4} -.337302{col 67}{space 3}  .048968
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  -.16857{col 26}{space 2} .0968015{col 37}{space 1}   -1.74{col 46}{space 3}0.082{col 54}{space 4}-.3582975{col 67}{space 3} .0211575
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.1608274{col 26}{space 2} .0972686{col 37}{space 1}   -1.65{col 46}{space 3}0.098{col 54}{space 4}-.3514703{col 67}{space 3} .0298155
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.1460455{col 26}{space 2} .0980576{col 37}{space 1}   -1.49{col 46}{space 3}0.136{col 54}{space 4} -.338235{col 67}{space 3} .0461439
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0297853{col 26}{space 2} .0623595{col 37}{space 1}   -0.48{col 46}{space 3}0.633{col 54}{space 4}-.1520077{col 67}{space 3} .0924371
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0020391{col 26}{space 2} .0027326{col 37}{space 1}    0.75{col 46}{space 3}0.456{col 54}{space 4}-.0033168{col 67}{space 3} .0073949
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0134282{col 26}{space 2} .0293588{col 37}{space 1}    0.46{col 46}{space 3}0.647{col 54}{space 4}-.0441139{col 67}{space 3} .0709703
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0546933{col 26}{space 2} .0812845{col 37}{space 1}    0.67{col 46}{space 3}0.501{col 54}{space 4}-.1046214{col 67}{space 3}  .214008
{txt}{space 6}income {c |}{col 14}{res}{space 2}  -.02577{col 26}{space 2} .0122499{col 37}{space 1}   -2.10{col 46}{space 3}0.035{col 54}{space 4}-.0497794{col 67}{space 3}-.0017606
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3773481{col 26}{space 2} .1013852{col 37}{space 1}    3.72{col 46}{space 3}0.000{col 54}{space 4} .1786367{col 67}{space 3} .5760595
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} -.041663{col 26}{space 2} .1492755{col 37}{space 1}   -0.28{col 46}{space 3}0.780{col 54}{space 4}-.3342376{col 67}{space 3} .2509116
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0431601{col 26}{space 2}  .168698{col 37}{space 1}    0.26{col 46}{space 3}0.798{col 54}{space 4} -.287482{col 67}{space 3} .3738022
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0362226{col 26}{space 2} .1524426{col 37}{space 1}    0.24{col 46}{space 3}0.812{col 54}{space 4}-.2625595{col 67}{space 3} .3350047
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1909079{col 26}{space 2}        .{col 37}{space 1}       .{col 46}{space 3}    .{col 54}{space 4}        .{col 67}{space 3}        .
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0290056{col 26}{space 2} .1380343{col 37}{space 1}    0.21{col 46}{space 3}0.834{col 54}{space 4}-.2415366{col 67}{space 3} .2995477
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.1281033{col 26}{space 2} .1551999{col 37}{space 1}   -0.83{col 46}{space 3}0.409{col 54}{space 4}-.4322894{col 67}{space 3} .1760829
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .1224145{col 26}{space 2} .1647089{col 37}{space 1}    0.74{col 46}{space 3}0.457{col 54}{space 4}-.2004091{col 67}{space 3} .4452381
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0174084{col 26}{space 2} .2465886{col 37}{space 1}    0.07{col 46}{space 3}0.944{col 54}{space 4}-.4658963{col 67}{space 3} .5007132
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Gamma        {txt}{c |}
{space 10}t2 {c |}{col 14}{res}{space 2}  .055653{col 26}{space 2} .0666951{col 37}{space 1}    0.83{col 46}{space 3}0.404{col 54}{space 4}-.0750671{col 67}{space 3} .1863731
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0084841{col 26}{space 2} .0664541{col 37}{space 1}   -0.13{col 46}{space 3}0.898{col 54}{space 4}-.1387318{col 67}{space 3} .1217635
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0157949{col 26}{space 2} .0651035{col 37}{space 1}    0.24{col 46}{space 3}0.808{col 54}{space 4}-.1118056{col 67}{space 3} .1433955
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0058386{col 26}{space 2} .0681947{col 37}{space 1}    0.09{col 46}{space 3}0.932{col 54}{space 4}-.1278205{col 67}{space 3} .1394978
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.1450333{col 26}{space 2} .0422051{col 37}{space 1}   -3.44{col 46}{space 3}0.001{col 54}{space 4}-.2277538{col 67}{space 3}-.0623129
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0103713{col 26}{space 2} .0017589{col 37}{space 1}   -5.90{col 46}{space 3}0.000{col 54}{space 4}-.0138186{col 67}{space 3} -.006924
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0398999{col 26}{space 2} .0201991{col 37}{space 1}   -1.98{col 46}{space 3}0.048{col 54}{space 4}-.0794894{col 67}{space 3}-.0003105
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.1463978{col 26}{space 2} .0533689{col 37}{space 1}   -2.74{col 46}{space 3}0.006{col 54}{space 4}-.2509988{col 67}{space 3}-.0417967
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0092903{col 26}{space 2} .0080581{col 37}{space 1}    1.15{col 46}{space 3}0.249{col 54}{space 4}-.0065033{col 67}{space 3}  .025084
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1957393{col 26}{space 2}  .068348{col 37}{space 1}    2.86{col 46}{space 3}0.004{col 54}{space 4} .0617796{col 67}{space 3}  .329699
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .6385909{col 26}{space 2} .0980969{col 37}{space 1}    6.51{col 46}{space 3}0.000{col 54}{space 4} .4463246{col 67}{space 3} .8308572
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .6298255{col 26}{space 2} .1100939{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .4140454{col 67}{space 3} .8456057
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}  .659392{col 26}{space 2} .0983534{col 37}{space 1}    6.70{col 46}{space 3}0.000{col 54}{space 4} .4666229{col 67}{space 3} .8521612
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .5957071{col 26}{space 2}        .{col 37}{space 1}       .{col 46}{space 3}    .{col 54}{space 4}        .{col 67}{space 3}        .
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .5716605{col 26}{space 2} .0879884{col 37}{space 1}    6.50{col 46}{space 3}0.000{col 54}{space 4} .3992065{col 67}{space 3} .7441146
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .7082025{col 26}{space 2} .0990388{col 37}{space 1}    7.15{col 46}{space 3}0.000{col 54}{space 4}   .51409{col 67}{space 3}  .902315
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .5436885{col 26}{space 2} .1062049{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4} .3355308{col 67}{space 3} .7518462
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6127993{col 26}{space 2}  .166519{col 37}{space 1}    3.68{col 46}{space 3}0.000{col 54}{space 4} .2864281{col 67}{space 3} .9391705
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 4 4}{txt}Instruments for equation {res}Delta{txt}: {res}t2 t3 t4 t5 female age education govt_emp income islam r2 r3 r4 r5 r6 r7 o.r8 _cons{p_end}
{p 0 4 4}{txt}Instruments for equation {res}Gamma{txt}: {res}t2 t3 t4 t5 female age education govt_emp income islam r2 r3 r4 r5 r6 r7 o.r8 _cons{p_end}
{txt}Warning: Convergence not achieved.
({res}est5{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3562}"

{com}. estadd local outcome "Kılıçdaroğlu vote intention", replace

{txt}added macro:
            e(outcome) : "{res:Kılıçdaroğlu vote intention}"

{com}. eststo: reg rpp t2-t5 female age education govt_emp income islam r2-r8, robust  
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,129
                                                {txt}F(16, 3112)       =  {res}     9.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0435
                                                {txt}Root MSE          =    {res} .47498

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}         rpp{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0307429{col 26}{space 2} .0268729{col 37}{space 1}   -1.14{col 46}{space 3}0.253{col 54}{space 4}-.0834334{col 67}{space 3} .0219476
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0111468{col 26}{space 2}  .026577{col 37}{space 1}   -0.42{col 46}{space 3}0.675{col 54}{space 4} -.063257{col 67}{space 3} .0409634
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0068129{col 26}{space 2}  .027271{col 37}{space 1}    0.25{col 46}{space 3}0.803{col 54}{space 4}-.0466581{col 67}{space 3}  .060284
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0166321{col 26}{space 2} .0271616{col 37}{space 1}    0.61{col 46}{space 3}0.540{col 54}{space 4}-.0366244{col 67}{space 3} .0698885
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0592744{col 26}{space 2} .0178934{col 37}{space 1}    3.31{col 46}{space 3}0.001{col 54}{space 4} .0241904{col 67}{space 3} .0943584
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0023221{col 26}{space 2} .0007561{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .0008397{col 67}{space 3} .0038046
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0137291{col 26}{space 2} .0079088{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0017779{col 67}{space 3}  .029236
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0674188{col 26}{space 2} .0234371{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4}  .021465{col 67}{space 3} .1133725
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0082425{col 26}{space 2} .0033946{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .0015867{col 67}{space 3} .0148983
{txt}{space 7}islam {c |}{col 14}{res}{space 2}-.1683622{col 26}{space 2} .0306845{col 37}{space 1}   -5.49{col 46}{space 3}0.000{col 54}{space 4}-.2285261{col 67}{space 3}-.1081982
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}   .22396{col 26}{space 2} .0391522{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .1471933{col 67}{space 3} .3007267
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .1373814{col 26}{space 2} .0430597{col 37}{space 1}    3.19{col 46}{space 3}0.001{col 54}{space 4} .0529531{col 67}{space 3} .2218097
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1140077{col 26}{space 2} .0387428{col 37}{space 1}    2.94{col 46}{space 3}0.003{col 54}{space 4} .0380438{col 67}{space 3} .1899717
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2} .1306816{col 26}{space 2}  .034623{col 37}{space 1}    3.77{col 46}{space 3}0.000{col 54}{space 4} .0627953{col 67}{space 3} .1985679
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .1212646{col 26}{space 2} .0403826{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .0420853{col 67}{space 3} .2004439
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0426019{col 26}{space 2} .0424179{col 37}{space 1}    1.00{col 46}{space 3}0.315{col 54}{space 4}-.0405681{col 67}{space 3} .1257719
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1538056{col 26}{space 2}  .064099{col 37}{space 1}    2.40{col 46}{space 3}0.016{col 54}{space 4} .0281251{col 67}{space 3} .2794862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3129}"

{com}. estadd local outcome "CHP vote intention", replace

{txt}added macro:
            e(outcome) : "{res:CHP vote intention}"

{com}. eststo: reg o7_std t2-t5 female age education govt_emp income islam r2-r8, robust       
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     3,657
                                                {txt}F(16, 3640)       =  {res}     5.11
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0214
                                                {txt}Root MSE          =    {res} .38254

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o7_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0049208{col 26}{space 2} .0204433{col 37}{space 1}   -0.24{col 46}{space 3}0.810{col 54}{space 4}-.0450023{col 67}{space 3} .0351606
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0433064{col 26}{space 2} .0200245{col 37}{space 1}   -2.16{col 46}{space 3}0.031{col 54}{space 4}-.0825667{col 67}{space 3} -.004046
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0173228{col 26}{space 2} .0204407{col 37}{space 1}   -0.85{col 46}{space 3}0.397{col 54}{space 4}-.0573991{col 67}{space 3} .0227536
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} -.023841{col 26}{space 2} .0200423{col 37}{space 1}   -1.19{col 46}{space 3}0.234{col 54}{space 4}-.0631362{col 67}{space 3} .0154542
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0460242{col 26}{space 2} .0133313{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .0198866{col 67}{space 3} .0721617
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .000467{col 26}{space 2} .0005786{col 37}{space 1}    0.81{col 46}{space 3}0.420{col 54}{space 4}-.0006674{col 67}{space 3} .0016014
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0035528{col 26}{space 2} .0060284{col 37}{space 1}    0.59{col 46}{space 3}0.556{col 54}{space 4}-.0082665{col 67}{space 3} .0153721
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0562213{col 26}{space 2} .0167674{col 37}{space 1}   -3.35{col 46}{space 3}0.001{col 54}{space 4}-.0890958{col 67}{space 3}-.0233468
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0025693{col 26}{space 2} .0025594{col 37}{space 1}    1.00{col 46}{space 3}0.315{col 54}{space 4}-.0024486{col 67}{space 3} .0075873
{txt}{space 7}islam {c |}{col 14}{res}{space 2}-.1194021{col 26}{space 2} .0224253{col 37}{space 1}   -5.32{col 46}{space 3}0.000{col 54}{space 4}-.1633696{col 67}{space 3}-.0754347
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0591758{col 26}{space 2} .0278596{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0045539{col 67}{space 3} .1137978
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0247835{col 26}{space 2} .0309771{col 37}{space 1}    0.80{col 46}{space 3}0.424{col 54}{space 4}-.0359507{col 67}{space 3} .0855177
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0279982{col 26}{space 2} .0277791{col 37}{space 1}    1.01{col 46}{space 3}0.314{col 54}{space 4}-.0264659{col 67}{space 3} .0824622
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0096144{col 26}{space 2} .0338626{col 37}{space 1}    0.28{col 46}{space 3}0.776{col 54}{space 4}-.0567773{col 67}{space 3}  .076006
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0646493{col 26}{space 2}  .024816{col 37}{space 1}    2.61{col 46}{space 3}0.009{col 54}{space 4} .0159946{col 67}{space 3}  .113304
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0308177{col 26}{space 2} .0289852{col 37}{space 1}    1.06{col 46}{space 3}0.288{col 54}{space 4}-.0260111{col 67}{space 3} .0876465
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .3460935{col 26}{space 2}  .049238{col 37}{space 1}    7.03{col 46}{space 3}0.000{col 54}{space 4} .2495567{col 67}{space 3} .4426303
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:3657}"

{com}. estadd local outcome "Kılıçdaroğlu volunteer intention", replace

{txt}added macro:
            e(outcome) : "{res:Kılıçdaroğlu volunteer intention}"

{com}. esttab using "`drive'/ATEothers.tex", replace ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(outcome i, label("Outcome" "Observations")) ///
>         title("Average Treatment Effects on Various Outcomes \label{c -(}tab:ATEothers{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/ATEothers.tex"'})

{com}. 
. 
. 
. *********************************************************
. *** Table I5: Treatment Effects Conditional on Education
. *********************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std i.t2##c.education female age govt_emp income islam r2-r8 if treatment==1 | treatment==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,507
                                                {txt}F(14, 1492)       =  {res}    11.26
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0641
                                                {txt}Root MSE          =    {res} .39191

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t2 {c |}{col 16}{res}{space 2}-.0486741{col 28}{space 2} .0796751{col 39}{space 1}   -0.61{col 48}{space 3}0.541{col 56}{space 4}-.2049613{col 69}{space 3}  .107613
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0277245{col 28}{space 2} .0130389{col 39}{space 1}   -2.13{col 48}{space 3}0.034{col 56}{space 4}-.0533011{col 69}{space 3}-.0021479
{txt}{space 14} {c |}
t2#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}  .002342{col 28}{space 2} .0178591{col 39}{space 1}    0.13{col 48}{space 3}0.896{col 56}{space 4}-.0326897{col 69}{space 3} .0373737
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2}  .032013{col 28}{space 2} .0209081{col 39}{space 1}    1.53{col 48}{space 3}0.126{col 56}{space 4}-.0089995{col 69}{space 3} .0730255
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0023984{col 28}{space 2} .0008631{col 39}{space 1}   -2.78{col 48}{space 3}0.006{col 56}{space 4}-.0040913{col 69}{space 3}-.0007054
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0080137{col 28}{space 2} .0270295{col 39}{space 1}   -0.30{col 48}{space 3}0.767{col 56}{space 4}-.0610336{col 69}{space 3} .0450062
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0004156{col 28}{space 2} .0040495{col 39}{space 1}   -0.10{col 48}{space 3}0.918{col 56}{space 4}-.0083589{col 69}{space 3} .0075276
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2750834{col 28}{space 2} .0281328{col 39}{space 1}    9.78{col 48}{space 3}0.000{col 56}{space 4} .2198994{col 69}{space 3} .3302674
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0661569{col 28}{space 2} .0451801{col 39}{space 1}   -1.46{col 48}{space 3}0.143{col 56}{space 4}-.1547802{col 69}{space 3} .0224665
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0521806{col 28}{space 2} .0515243{col 39}{space 1}    1.01{col 48}{space 3}0.311{col 56}{space 4}-.0488872{col 69}{space 3} .1532484
{txt}{space 12}r4 {c |}{col 16}{res}{space 2}  .049976{col 28}{space 2} .0479882{col 39}{space 1}    1.04{col 48}{space 3}0.298{col 56}{space 4}-.0441555{col 69}{space 3} .1441074
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .1041845{col 28}{space 2} .0601737{col 39}{space 1}    1.73{col 48}{space 3}0.084{col 56}{space 4}-.0138495{col 69}{space 3} .2222186
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0210291{col 28}{space 2} .0424021{col 39}{space 1}    0.50{col 48}{space 3}0.620{col 56}{space 4}-.0621448{col 69}{space 3} .1042031
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}  .040414{col 28}{space 2} .0490961{col 39}{space 1}    0.82{col 48}{space 3}0.411{col 56}{space 4}-.0558907{col 69}{space 3} .1367186
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3506432{col 28}{space 2} .0815891{col 39}{space 1}    4.30{col 48}{space 3}0.000{col 56}{space 4} .1906018{col 69}{space 3} .5106847
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1507}"

{com}. eststo: reg o1_std i.t2##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==1 | treatment==2 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       752
                                                {txt}F(14, 737)        =  {res}     5.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0732
                                                {txt}Root MSE          =    {res} .38974

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t2 {c |}{col 16}{res}{space 2}-.0342147{col 28}{space 2} .1111706{col 39}{space 1}   -0.31{col 48}{space 3}0.758{col 56}{space 4}-.2524636{col 69}{space 3} .1840341
{txt}{space 5}education {c |}{col 16}{res}{space 2} -.016802{col 28}{space 2} .0194166{col 39}{space 1}   -0.87{col 48}{space 3}0.387{col 56}{space 4}-.0549204{col 69}{space 3} .0213165
{txt}{space 14} {c |}
t2#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0113398{col 28}{space 2} .0250172{col 39}{space 1}   -0.45{col 48}{space 3}0.650{col 56}{space 4}-.0604532{col 69}{space 3} .0377736
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0352002{col 28}{space 2}  .030221{col 39}{space 1}    1.16{col 48}{space 3}0.244{col 56}{space 4}-.0241293{col 69}{space 3} .0945296
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0030533{col 28}{space 2} .0012646{col 39}{space 1}   -2.41{col 48}{space 3}0.016{col 56}{space 4}-.0055359{col 69}{space 3}-.0005707
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2} .0111956{col 28}{space 2}  .039652{col 39}{space 1}    0.28{col 48}{space 3}0.778{col 56}{space 4}-.0666486{col 69}{space 3} .0890399
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0020102{col 28}{space 2} .0057544{col 39}{space 1}   -0.35{col 48}{space 3}0.727{col 56}{space 4}-.0133072{col 69}{space 3} .0092868
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2597857{col 28}{space 2} .0409339{col 39}{space 1}    6.35{col 48}{space 3}0.000{col 56}{space 4} .1794248{col 69}{space 3} .3401467
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0012975{col 28}{space 2}  .062563{col 39}{space 1}   -0.02{col 48}{space 3}0.983{col 56}{space 4}-.1241204{col 69}{space 3} .1215253
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .1531485{col 28}{space 2} .0723252{col 39}{space 1}    2.12{col 48}{space 3}0.035{col 56}{space 4} .0111606{col 69}{space 3} .2951363
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0857552{col 28}{space 2} .0667445{col 39}{space 1}    1.28{col 48}{space 3}0.199{col 56}{space 4}-.0452768{col 69}{space 3} .2167872
{txt}{space 12}r5 {c |}{col 16}{res}{space 2}  .124714{col 28}{space 2}  .081051{col 39}{space 1}    1.54{col 48}{space 3}0.124{col 56}{space 4}-.0344044{col 69}{space 3} .2838323
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0665067{col 28}{space 2} .0589822{col 39}{space 1}    1.13{col 48}{space 3}0.260{col 56}{space 4}-.0492864{col 69}{space 3} .1822998
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0724407{col 28}{space 2} .0674373{col 39}{space 1}    1.07{col 48}{space 3}0.283{col 56}{space 4}-.0599513{col 69}{space 3} .2048328
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3111332{col 28}{space 2} .1177505{col 39}{space 1}    2.64{col 48}{space 3}0.008{col 56}{space 4} .0799669{col 69}{space 3} .5422996
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:752}"

{com}. eststo: reg o1_std i.t2##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==2 | treatment==2 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       755
                                                {txt}F(14, 740)        =  {res}     6.57
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0709
                                                {txt}Root MSE          =    {res} .39429

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t2 {c |}{col 16}{res}{space 2}-.0523471{col 28}{space 2} .1161685{col 39}{space 1}   -0.45{col 48}{space 3}0.652{col 56}{space 4}-.2804061{col 69}{space 3} .1757119
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0365917{col 28}{space 2}  .017741{col 39}{space 1}   -2.06{col 48}{space 3}0.040{col 56}{space 4}-.0714203{col 69}{space 3} -.001763
{txt}{space 14} {c |}
t2#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .0131155{col 28}{space 2} .0259642{col 39}{space 1}    0.51{col 48}{space 3}0.614{col 56}{space 4}-.0378569{col 69}{space 3} .0640878
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0306567{col 28}{space 2} .0291859{col 39}{space 1}    1.05{col 48}{space 3}0.294{col 56}{space 4}-.0266403{col 69}{space 3} .0879536
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0018276{col 28}{space 2} .0011818{col 39}{space 1}   -1.55{col 48}{space 3}0.122{col 56}{space 4}-.0041476{col 69}{space 3} .0004925
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0175172{col 28}{space 2} .0379303{col 39}{space 1}   -0.46{col 48}{space 3}0.644{col 56}{space 4} -.091981{col 69}{space 3} .0569467
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0013139{col 28}{space 2} .0057659{col 39}{space 1}    0.23{col 48}{space 3}0.820{col 56}{space 4}-.0100055{col 69}{space 3} .0126333
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2856135{col 28}{space 2} .0391763{col 39}{space 1}    7.29{col 48}{space 3}0.000{col 56}{space 4} .2087035{col 69}{space 3} .3625235
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.1349695{col 28}{space 2} .0654001{col 39}{space 1}   -2.06{col 48}{space 3}0.039{col 56}{space 4}-.2633614{col 69}{space 3}-.0065777
{txt}{space 12}r3 {c |}{col 16}{res}{space 2}-.0641522{col 28}{space 2} .0733273{col 39}{space 1}   -0.87{col 48}{space 3}0.382{col 56}{space 4}-.2081064{col 69}{space 3} .0798021
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0043085{col 28}{space 2} .0687417{col 39}{space 1}    0.06{col 48}{space 3}0.950{col 56}{space 4}-.1306434{col 69}{space 3} .1392604
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .0719764{col 28}{space 2} .0884977{col 39}{space 1}    0.81{col 48}{space 3}0.416{col 56}{space 4}-.1017602{col 69}{space 3} .2457129
{txt}{space 12}r6 {c |}{col 16}{res}{space 2}-.0354584{col 28}{space 2} .0608942{col 39}{space 1}   -0.58{col 48}{space 3}0.561{col 56}{space 4}-.1550044{col 69}{space 3} .0840877
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.0020789{col 28}{space 2} .0713105{col 39}{space 1}   -0.03{col 48}{space 3}0.977{col 56}{space 4}-.1420739{col 69}{space 3} .1379162
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2}   .39359{col 28}{space 2} .1138203{col 39}{space 1}    3.46{col 48}{space 3}0.001{col 56}{space 4} .1701407{col 69}{space 3} .6170392
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:755}"

{com}. eststo: reg o1_std i.t3##c.education female age govt_emp income islam r2-r8 if treatment==1 | treatment==3, robust

{txt}Linear regression                               Number of obs     = {res}     1,536
                                                {txt}{help j_robustsingular:F(14, 1520) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0707
                                                {txt}Root MSE          =    {res} .38653

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t3 {c |}{col 16}{res}{space 2}-.0003781{col 28}{space 2} .0783594{col 39}{space 1}   -0.00{col 48}{space 3}0.996{col 56}{space 4}-.1540821{col 69}{space 3} .1533259
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0296539{col 28}{space 2} .0129276{col 39}{space 1}   -2.29{col 48}{space 3}0.022{col 56}{space 4}-.0550117{col 69}{space 3}-.0042961
{txt}{space 14} {c |}
t3#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}  .001529{col 28}{space 2} .0173995{col 39}{space 1}    0.09{col 48}{space 3}0.930{col 56}{space 4}-.0326005{col 69}{space 3} .0356586
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0217534{col 28}{space 2} .0204828{col 39}{space 1}    1.06{col 48}{space 3}0.288{col 56}{space 4}-.0184242{col 69}{space 3}  .061931
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0015989{col 28}{space 2} .0008667{col 39}{space 1}   -1.84{col 48}{space 3}0.065{col 56}{space 4}-.0032989{col 69}{space 3} .0001011
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0137834{col 28}{space 2} .0265758{col 39}{space 1}   -0.52{col 48}{space 3}0.604{col 56}{space 4}-.0659125{col 69}{space 3} .0383457
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0021499{col 28}{space 2} .0039833{col 39}{space 1}    0.54{col 48}{space 3}0.589{col 56}{space 4}-.0056635{col 69}{space 3} .0099632
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2795717{col 28}{space 2} .0277303{col 39}{space 1}   10.08{col 48}{space 3}0.000{col 56}{space 4}  .225178{col 69}{space 3} .3339654
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.1400272{col 28}{space 2} .0301225{col 39}{space 1}   -4.65{col 48}{space 3}0.000{col 56}{space 4}-.1991132{col 69}{space 3}-.0809411
{txt}{space 12}r3 {c |}{col 16}{res}{space 2}-.0624917{col 28}{space 2} .0387687{col 39}{space 1}   -1.61{col 48}{space 3}0.107{col 56}{space 4}-.1385375{col 69}{space 3} .0135541
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0159351{col 28}{space 2} .0330026{col 39}{space 1}    0.48{col 48}{space 3}0.629{col 56}{space 4}-.0488003{col 69}{space 3} .0806705
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .0303962{col 28}{space 2} .0445489{col 39}{space 1}    0.68{col 48}{space 3}0.495{col 56}{space 4}-.0569875{col 69}{space 3}   .11778
{txt}{space 12}r6 {c |}{col 16}{res}{space 2}-.0631946{col 28}{space 2} .0242854{col 39}{space 1}   -2.60{col 48}{space 3}0.009{col 56}{space 4}-.1108311{col 69}{space 3} -.015558
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.0844429{col 28}{space 2} .0334638{col 39}{space 1}   -2.52{col 48}{space 3}0.012{col 56}{space 4}-.1500831{col 69}{space 3}-.0188027
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}-.0331253{col 28}{space 2} .0415574{col 39}{space 1}   -0.80{col 48}{space 3}0.426{col 56}{space 4}-.1146413{col 69}{space 3} .0483907
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .3900497{col 28}{space 2} .0747399{col 39}{space 1}    5.22{col 48}{space 3}0.000{col 56}{space 4} .2434454{col 69}{space 3} .5366539
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1536}"

{com}. eststo: reg o1_std i.t3##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==1 | treatment==3 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       768
                                                {txt}F(14, 753)        =  {res}     5.33
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0735
                                                {txt}Root MSE          =    {res} .38984

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t3 {c |}{col 16}{res}{space 2} .0748779{col 28}{space 2} .1170449{col 39}{space 1}    0.64{col 48}{space 3}0.523{col 56}{space 4}-.1548952{col 69}{space 3}  .304651
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0210484{col 28}{space 2} .0193492{col 39}{space 1}   -1.09{col 48}{space 3}0.277{col 56}{space 4}-.0590332{col 69}{space 3} .0169364
{txt}{space 14} {c |}
t3#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0188551{col 28}{space 2} .0257091{col 39}{space 1}   -0.73{col 48}{space 3}0.464{col 56}{space 4}-.0693252{col 69}{space 3} .0316151
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0142134{col 28}{space 2}  .029625{col 39}{space 1}    0.48{col 48}{space 3}0.632{col 56}{space 4}-.0439441{col 69}{space 3} .0723708
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0032802{col 28}{space 2} .0013007{col 39}{space 1}   -2.52{col 48}{space 3}0.012{col 56}{space 4}-.0058335{col 69}{space 3}-.0007268
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0042367{col 28}{space 2} .0419285{col 39}{space 1}   -0.10{col 48}{space 3}0.920{col 56}{space 4}-.0865474{col 69}{space 3}  .078074
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0021918{col 28}{space 2} .0058216{col 39}{space 1}    0.38{col 48}{space 3}0.707{col 56}{space 4}-.0092367{col 69}{space 3} .0136203
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2869577{col 28}{space 2} .0413409{col 39}{space 1}    6.94{col 48}{space 3}0.000{col 56}{space 4} .2058006{col 69}{space 3} .3681148
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0386567{col 28}{space 2} .0643129{col 39}{space 1}   -0.60{col 48}{space 3}0.548{col 56}{space 4}-.1649106{col 69}{space 3} .0875973
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0787004{col 28}{space 2} .0719918{col 39}{space 1}    1.09{col 48}{space 3}0.275{col 56}{space 4}-.0626281{col 69}{space 3} .2200288
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .1046004{col 28}{space 2}  .066438{col 39}{space 1}    1.57{col 48}{space 3}0.116{col 56}{space 4}-.0258253{col 69}{space 3} .2350261
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .0649983{col 28}{space 2} .0759011{col 39}{space 1}    0.86{col 48}{space 3}0.392{col 56}{space 4}-.0840046{col 69}{space 3} .2140012
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0524954{col 28}{space 2} .0592043{col 39}{space 1}    0.89{col 48}{space 3}0.376{col 56}{space 4}-.0637298{col 69}{space 3} .1687206
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0272104{col 28}{space 2} .0671176{col 39}{space 1}    0.41{col 48}{space 3}0.685{col 56}{space 4}-.1045494{col 69}{space 3} .1589702
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3191254{col 28}{space 2}  .119018{col 39}{space 1}    2.68{col 48}{space 3}0.007{col 56}{space 4} .0854788{col 69}{space 3} .5527719
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:768}"

{com}. eststo: reg o1_std i.t3##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==2 | treatment==3 & order==2, robust

{txt}Linear regression                               Number of obs     = {res}       768
                                                {txt}{help j_robustsingular:F(14, 752) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0832
                                                {txt}Root MSE          =    {res} .38383

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t3 {c |}{col 16}{res}{space 2}-.0506252{col 28}{space 2} .1060539{col 39}{space 1}   -0.48{col 48}{space 3}0.633{col 56}{space 4}-.2588221{col 69}{space 3} .1575718
{txt}{space 5}education {c |}{col 16}{res}{space 2} -.036071{col 28}{space 2} .0174517{col 39}{space 1}   -2.07{col 48}{space 3}0.039{col 56}{space 4}-.0703309{col 69}{space 3}-.0018111
{txt}{space 14} {c |}
t3#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .0165399{col 28}{space 2} .0238221{col 39}{space 1}    0.69{col 48}{space 3}0.488{col 56}{space 4}-.0302257{col 69}{space 3} .0633055
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0282095{col 28}{space 2} .0284386{col 39}{space 1}    0.99{col 48}{space 3}0.322{col 56}{space 4}-.0276189{col 69}{space 3}  .084038
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0003152{col 28}{space 2} .0011438{col 39}{space 1}   -0.28{col 48}{space 3}0.783{col 56}{space 4}-.0025607{col 69}{space 3} .0019303
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0160404{col 28}{space 2} .0352391{col 39}{space 1}   -0.46{col 48}{space 3}0.649{col 56}{space 4}-.0852191{col 69}{space 3} .0531382
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0016549{col 28}{space 2} .0055041{col 39}{space 1}    0.30{col 48}{space 3}0.764{col 56}{space 4}-.0091502{col 69}{space 3} .0124601
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2691991{col 28}{space 2} .0374865{col 39}{space 1}    7.18{col 48}{space 3}0.000{col 56}{space 4} .1956084{col 69}{space 3} .3427898
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.1439391{col 28}{space 2} .0430932{col 39}{space 1}   -3.34{col 48}{space 3}0.001{col 56}{space 4}-.2285364{col 69}{space 3}-.0593419
{txt}{space 12}r3 {c |}{col 16}{res}{space 2}-.1113611{col 28}{space 2}  .054625{col 39}{space 1}   -2.04{col 48}{space 3}0.042{col 56}{space 4}-.2185967{col 69}{space 3}-.0041256
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0299085{col 28}{space 2} .0456711{col 39}{space 1}    0.65{col 48}{space 3}0.513{col 56}{space 4}-.0597494{col 69}{space 3} .1195664
{txt}{space 12}r5 {c |}{col 16}{res}{space 2}  .104477{col 28}{space 2} .0668599{col 39}{space 1}    1.56{col 48}{space 3}0.119{col 56}{space 4}-.0267772{col 69}{space 3} .2357312
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} -.080945{col 28}{space 2} .0341786{col 39}{space 1}   -2.37{col 48}{space 3}0.018{col 56}{space 4}-.1480417{col 69}{space 3}-.0138482
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.0954379{col 28}{space 2} .0467715{col 39}{space 1}   -2.04{col 48}{space 3}0.042{col 56}{space 4} -.187256{col 69}{space 3}-.0036197
{txt}{space 12}r8 {c |}{col 16}{res}{space 2} .0217991{col 28}{space 2} .0575418{col 39}{space 1}    0.38{col 48}{space 3}0.705{col 56}{space 4}-.0911627{col 69}{space 3} .1347609
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .3736433{col 28}{space 2} .1016798{col 39}{space 1}    3.67{col 48}{space 3}0.000{col 56}{space 4} .1740332{col 69}{space 3} .5732534
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:768}"

{com}. eststo: reg o1_std i.t4##c.education female age govt_emp income islam r2-r8 if treatment==1 | treatment==4, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,521
                                                {txt}F(14, 1506)       =  {res}    15.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0825
                                                {txt}Root MSE          =    {res} .38703

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t4 {c |}{col 16}{res}{space 2} .0836577{col 28}{space 2} .0782177{col 39}{space 1}    1.07{col 48}{space 3}0.285{col 56}{space 4}-.0697696{col 69}{space 3} .2370849
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0261174{col 28}{space 2} .0130378{col 39}{space 1}   -2.00{col 48}{space 3}0.045{col 56}{space 4}-.0516916{col 69}{space 3}-.0005432
{txt}{space 14} {c |}
t4#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0248364{col 28}{space 2} .0174953{col 39}{space 1}   -1.42{col 48}{space 3}0.156{col 56}{space 4}-.0591542{col 69}{space 3} .0094814
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0244606{col 28}{space 2}  .020479{col 39}{space 1}    1.19{col 48}{space 3}0.233{col 56}{space 4}-.0157098{col 69}{space 3} .0646309
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0023229{col 28}{space 2} .0008801{col 39}{space 1}   -2.64{col 48}{space 3}0.008{col 56}{space 4}-.0040492{col 69}{space 3}-.0005967
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0064891{col 28}{space 2}  .027058{col 39}{space 1}   -0.24{col 48}{space 3}0.811{col 56}{space 4}-.0595645{col 69}{space 3} .0465862
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0002825{col 28}{space 2} .0039799{col 39}{space 1}   -0.07{col 48}{space 3}0.943{col 56}{space 4}-.0080892{col 69}{space 3} .0075241
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2887082{col 28}{space 2} .0260596{col 39}{space 1}   11.08{col 48}{space 3}0.000{col 56}{space 4} .2375912{col 69}{space 3} .3398252
{txt}{space 12}r2 {c |}{col 16}{res}{space 2} -.078369{col 28}{space 2} .0481384{col 39}{space 1}   -1.63{col 48}{space 3}0.104{col 56}{space 4}-.1727945{col 69}{space 3} .0160565
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0386013{col 28}{space 2} .0521506{col 39}{space 1}    0.74{col 48}{space 3}0.459{col 56}{space 4}-.0636943{col 69}{space 3} .1408968
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0329095{col 28}{space 2} .0495892{col 39}{space 1}    0.66{col 48}{space 3}0.507{col 56}{space 4}-.0643618{col 69}{space 3} .1301808
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .0758883{col 28}{space 2} .0565344{col 39}{space 1}    1.34{col 48}{space 3}0.180{col 56}{space 4}-.0350062{col 69}{space 3} .1867829
{txt}{space 12}r6 {c |}{col 16}{res}{space 2}-.0177422{col 28}{space 2} .0439998{col 39}{space 1}   -0.40{col 48}{space 3}0.687{col 56}{space 4}-.1040496{col 69}{space 3} .0685653
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.0110185{col 28}{space 2} .0496759{col 39}{space 1}   -0.22{col 48}{space 3}0.824{col 56}{space 4}-.1084598{col 69}{space 3} .0864229
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3581109{col 28}{space 2} .0816901{col 39}{space 1}    4.38{col 48}{space 3}0.000{col 56}{space 4} .1978724{col 69}{space 3} .5183494
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1521}"

{com}. eststo: reg o1_std i.t4##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==1 | treatment==4 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       762
                                                {txt}F(14, 747)        =  {res}     9.25
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1100
                                                {txt}Root MSE          =    {res} .38335

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t4 {c |}{col 16}{res}{space 2} .0980835{col 28}{space 2} .1098361{col 39}{space 1}    0.89{col 48}{space 3}0.372{col 56}{space 4}-.1175407{col 69}{space 3} .3137077
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0214104{col 28}{space 2} .0194818{col 39}{space 1}   -1.10{col 48}{space 3}0.272{col 56}{space 4}-.0596559{col 69}{space 3} .0168351
{txt}{space 14} {c |}
t4#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0367267{col 28}{space 2} .0247221{col 39}{space 1}   -1.49{col 48}{space 3}0.138{col 56}{space 4}-.0852597{col 69}{space 3} .0118064
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0473659{col 28}{space 2} .0291255{col 39}{space 1}    1.63{col 48}{space 3}0.104{col 56}{space 4}-.0098117{col 69}{space 3} .1045435
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0044478{col 28}{space 2} .0012458{col 39}{space 1}   -3.57{col 48}{space 3}0.000{col 56}{space 4}-.0068934{col 69}{space 3}-.0020022
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0261689{col 28}{space 2} .0395354{col 39}{space 1}   -0.66{col 48}{space 3}0.508{col 56}{space 4}-.1037826{col 69}{space 3} .0514448
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0004108{col 28}{space 2} .0056248{col 39}{space 1}   -0.07{col 48}{space 3}0.942{col 56}{space 4}-.0114531{col 69}{space 3} .0106314
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .3011966{col 28}{space 2} .0380615{col 39}{space 1}    7.91{col 48}{space 3}0.000{col 56}{space 4} .2264764{col 69}{space 3} .3759169
{txt}{space 12}r2 {c |}{col 16}{res}{space 2} .0068977{col 28}{space 2} .0675753{col 39}{space 1}    0.10{col 48}{space 3}0.919{col 56}{space 4}-.1257625{col 69}{space 3} .1395579
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .1441094{col 28}{space 2} .0741019{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.0013633{col 69}{space 3} .2895821
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .1203726{col 28}{space 2} .0694152{col 39}{space 1}    1.73{col 48}{space 3}0.083{col 56}{space 4}-.0158994{col 69}{space 3} .2566447
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .2067239{col 28}{space 2} .0737967{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .0618504{col 69}{space 3} .3515975
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0715855{col 28}{space 2} .0614699{col 39}{space 1}    1.16{col 48}{space 3}0.245{col 56}{space 4}-.0490888{col 69}{space 3} .1922598
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0868557{col 28}{space 2} .0696787{col 39}{space 1}    1.25{col 48}{space 3}0.213{col 56}{space 4}-.0499337{col 69}{space 3} .2236451
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3218703{col 28}{space 2} .1178116{col 39}{space 1}    2.73{col 48}{space 3}0.006{col 56}{space 4}  .090589{col 69}{space 3} .5531516
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est8{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:762}"

{com}. eststo: reg o1_std i.t4##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==2 | treatment==4 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       759
                                                {txt}F(14, 744)        =  {res}     8.05
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0776
                                                {txt}Root MSE          =    {res} .38962

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t4 {c |}{col 16}{res}{space 2} .0669063{col 28}{space 2} .1125423{col 39}{space 1}    0.59{col 48}{space 3}0.552{col 56}{space 4} -.154032{col 69}{space 3} .2878446
{txt}{space 5}education {c |}{col 16}{res}{space 2} -.030727{col 28}{space 2} .0177161{col 39}{space 1}   -1.73{col 48}{space 3}0.083{col 56}{space 4}-.0655064{col 69}{space 3} .0040524
{txt}{space 14} {c |}
t4#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0120352{col 28}{space 2} .0250329{col 39}{space 1}   -0.48{col 48}{space 3}0.631{col 56}{space 4}-.0611788{col 69}{space 3} .0371085
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0012816{col 28}{space 2} .0289989{col 39}{space 1}    0.04{col 48}{space 3}0.965{col 56}{space 4}-.0556478{col 69}{space 3} .0582111
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0006148{col 28}{space 2} .0012264{col 39}{space 1}   -0.50{col 48}{space 3}0.616{col 56}{space 4}-.0030224{col 69}{space 3} .0017928
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2} .0184675{col 28}{space 2} .0379371{col 39}{space 1}    0.49{col 48}{space 3}0.627{col 56}{space 4} -.056009{col 69}{space 3}  .092944
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0004462{col 28}{space 2}  .005666{col 39}{space 1}   -0.08{col 48}{space 3}0.937{col 56}{space 4}-.0115695{col 69}{space 3}  .010677
{txt}{space 9}islam {c |}{col 16}{res}{space 2}  .290083{col 28}{space 2} .0359947{col 39}{space 1}    8.06{col 48}{space 3}0.000{col 56}{space 4} .2194197{col 69}{space 3} .3607464
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.1572786{col 28}{space 2} .0677441{col 39}{space 1}   -2.32{col 48}{space 3}0.021{col 56}{space 4}-.2902709{col 69}{space 3}-.0242863
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} -.066691{col 28}{space 2} .0721435{col 39}{space 1}   -0.92{col 48}{space 3}0.356{col 56}{space 4}  -.20832{col 69}{space 3}  .074938
{txt}{space 12}r4 {c |}{col 16}{res}{space 2}-.0502051{col 28}{space 2} .0695162{col 39}{space 1}   -0.72{col 48}{space 3}0.470{col 56}{space 4}-.1866764{col 69}{space 3} .0862661
{txt}{space 12}r5 {c |}{col 16}{res}{space 2}-.0797724{col 28}{space 2} .0882941{col 39}{space 1}   -0.90{col 48}{space 3}0.367{col 56}{space 4}-.2531076{col 69}{space 3} .0935627
{txt}{space 12}r6 {c |}{col 16}{res}{space 2}-.1053657{col 28}{space 2} .0615439{col 39}{space 1}   -1.71{col 48}{space 3}0.087{col 56}{space 4} -.226186{col 69}{space 3} .0154547
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.1051347{col 28}{space 2} .0699745{col 39}{space 1}   -1.50{col 48}{space 3}0.133{col 56}{space 4}-.2425056{col 69}{space 3} .0322362
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3950595{col 28}{space 2} .1140702{col 39}{space 1}    3.46{col 48}{space 3}0.001{col 56}{space 4} .1711217{col 69}{space 3} .6189972
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est9{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:759}"

{com}. eststo: reg o1_std i.t5##c.education female age govt_emp income islam r2-r8 if treatment==1 | treatment==5, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,537
                                                {txt}F(14, 1522)       =  {res}    18.50
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0867
                                                {txt}Root MSE          =    {res} .38867

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t5 {c |}{col 16}{res}{space 2}-.0430525{col 28}{space 2} .0810038{col 39}{space 1}   -0.53{col 48}{space 3}0.595{col 56}{space 4}-.2019433{col 69}{space 3} .1158383
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0224367{col 28}{space 2} .0129443{col 39}{space 1}   -1.73{col 48}{space 3}0.083{col 56}{space 4}-.0478272{col 69}{space 3} .0029539
{txt}{space 14} {c |}
t5#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .0107426{col 28}{space 2} .0179738{col 39}{space 1}    0.60{col 48}{space 3}0.550{col 56}{space 4}-.0245135{col 69}{space 3} .0459987
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2}  .049565{col 28}{space 2} .0205563{col 39}{space 1}    2.41{col 48}{space 3}0.016{col 56}{space 4} .0092434{col 69}{space 3} .0898866
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0016204{col 28}{space 2}  .000873{col 39}{space 1}   -1.86{col 48}{space 3}0.064{col 56}{space 4}-.0033329{col 69}{space 3} .0000921
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2} .0192671{col 28}{space 2} .0274167{col 39}{space 1}    0.70{col 48}{space 3}0.482{col 56}{space 4}-.0345114{col 69}{space 3} .0730455
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0030157{col 28}{space 2} .0039177{col 39}{space 1}   -0.77{col 48}{space 3}0.442{col 56}{space 4}-.0107004{col 69}{space 3}  .004669
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .3311948{col 28}{space 2} .0247645{col 39}{space 1}   13.37{col 48}{space 3}0.000{col 56}{space 4} .2826187{col 69}{space 3}  .379771
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0844178{col 28}{space 2} .0449286{col 39}{space 1}   -1.88{col 48}{space 3}0.060{col 56}{space 4}-.1725463{col 69}{space 3} .0037107
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0655943{col 28}{space 2} .0507877{col 39}{space 1}    1.29{col 48}{space 3}0.197{col 56}{space 4}-.0340271{col 69}{space 3} .1652156
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .0531745{col 28}{space 2} .0472982{col 39}{space 1}    1.12{col 48}{space 3}0.261{col 56}{space 4} -.039602{col 69}{space 3}  .145951
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .1249592{col 28}{space 2} .0582662{col 39}{space 1}    2.14{col 48}{space 3}0.032{col 56}{space 4} .0106688{col 69}{space 3} .2392497
{txt}{space 12}r6 {c |}{col 16}{res}{space 2}-.0032978{col 28}{space 2} .0415024{col 39}{space 1}   -0.08{col 48}{space 3}0.937{col 56}{space 4}-.0847057{col 69}{space 3} .0781101
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.0038773{col 28}{space 2} .0475715{col 39}{space 1}   -0.08{col 48}{space 3}0.935{col 56}{space 4}  -.09719{col 69}{space 3} .0894353
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .2684751{col 28}{space 2} .0794517{col 39}{space 1}    3.38{col 48}{space 3}0.001{col 56}{space 4} .1126287{col 69}{space 3} .4243214
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est10{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1537}"

{com}. eststo: reg o1_std i.t5##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==1 | treatment==5 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       756
                                                {txt}F(14, 741)        =  {res}     6.53
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0809
                                                {txt}Root MSE          =    {res} .39212

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t5 {c |}{col 16}{res}{space 2} .0108669{col 28}{space 2} .1174487{col 39}{space 1}    0.09{col 48}{space 3}0.926{col 56}{space 4} -.219705{col 69}{space 3} .2414388
{txt}{space 5}education {c |}{col 16}{res}{space 2} -.017824{col 28}{space 2} .0194833{col 39}{space 1}   -0.91{col 48}{space 3}0.361{col 56}{space 4} -.056073{col 69}{space 3} .0204251
{txt}{space 14} {c |}
t5#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2}-.0022301{col 28}{space 2} .0259462{col 39}{space 1}   -0.09{col 48}{space 3}0.932{col 56}{space 4}-.0531669{col 69}{space 3} .0487067
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0342465{col 28}{space 2} .0298401{col 39}{space 1}    1.15{col 48}{space 3}0.251{col 56}{space 4}-.0243346{col 69}{space 3} .0928277
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0026543{col 28}{space 2} .0012879{col 39}{space 1}   -2.06{col 48}{space 3}0.040{col 56}{space 4}-.0051827{col 69}{space 3}-.0001259
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2}-.0226835{col 28}{space 2} .0412286{col 39}{space 1}   -0.55{col 48}{space 3}0.582{col 56}{space 4}-.1036222{col 69}{space 3} .0582552
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0051802{col 28}{space 2} .0057371{col 39}{space 1}   -0.90{col 48}{space 3}0.367{col 56}{space 4}-.0164431{col 69}{space 3} .0060827
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .2998128{col 28}{space 2}  .043526{col 39}{space 1}    6.89{col 48}{space 3}0.000{col 56}{space 4} .2143638{col 69}{space 3} .3852618
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.0399892{col 28}{space 2} .0645381{col 39}{space 1}   -0.62{col 48}{space 3}0.536{col 56}{space 4}-.1666886{col 69}{space 3} .0867102
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0814878{col 28}{space 2}  .073093{col 39}{space 1}    1.11{col 48}{space 3}0.265{col 56}{space 4}-.0620064{col 69}{space 3} .2249819
{txt}{space 12}r4 {c |}{col 16}{res}{space 2} .1331404{col 28}{space 2} .0695585{col 39}{space 1}    1.91{col 48}{space 3}0.056{col 56}{space 4}-.0034147{col 69}{space 3} .2696955
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .1961361{col 28}{space 2} .0797602{col 39}{space 1}    2.46{col 48}{space 3}0.014{col 56}{space 4} .0395532{col 69}{space 3}  .352719
{txt}{space 12}r6 {c |}{col 16}{res}{space 2} .0610197{col 28}{space 2} .0606138{col 39}{space 1}    1.01{col 48}{space 3}0.314{col 56}{space 4}-.0579756{col 69}{space 3}  .180015
{txt}{space 12}r7 {c |}{col 16}{res}{space 2} .0635115{col 28}{space 2} .0678099{col 39}{space 1}    0.94{col 48}{space 3}0.349{col 56}{space 4}-.0696108{col 69}{space 3} .1966339
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .3002075{col 28}{space 2} .1197797{col 39}{space 1}    2.51{col 48}{space 3}0.012{col 56}{space 4} .0650596{col 69}{space 3} .5353555
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est11{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:756}"

{com}. eststo: reg o1_std i.t5##c.education female age govt_emp income islam r2-r8 if treatment==1 & order==2 | treatment==5 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       781
                                                {txt}F(14, 766)        =  {res}    15.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1051
                                                {txt}Root MSE          =    {res} .38613

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}        o1_std{col 16}{c |} Coefficient{col 28}  std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.t5 {c |}{col 16}{res}{space 2}-.1206077{col 28}{space 2} .1131261{col 39}{space 1}   -1.07{col 48}{space 3}0.287{col 56}{space 4}-.3426817{col 69}{space 3} .1014663
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.0277686{col 28}{space 2} .0174443{col 39}{space 1}   -1.59{col 48}{space 3}0.112{col 56}{space 4}-.0620129{col 69}{space 3} .0064757
{txt}{space 14} {c |}
t5#c.education {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .0292715{col 28}{space 2} .0252472{col 39}{space 1}    1.16{col 48}{space 3}0.247{col 56}{space 4}-.0202903{col 69}{space 3} .0788333
{txt}{space 14} {c |}
{space 8}female {c |}{col 16}{res}{space 2} .0652796{col 28}{space 2} .0285213{col 39}{space 1}    2.29{col 48}{space 3}0.022{col 56}{space 4} .0092905{col 69}{space 3} .1212687
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0007235{col 28}{space 2} .0011832{col 39}{space 1}   -0.61{col 48}{space 3}0.541{col 56}{space 4}-.0030462{col 69}{space 3} .0015992
{txt}{space 6}govt_emp {c |}{col 16}{res}{space 2} .0594331{col 28}{space 2} .0369377{col 39}{space 1}    1.61{col 48}{space 3}0.108{col 56}{space 4}-.0130781{col 69}{space 3} .1319443
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0007365{col 28}{space 2} .0054469{col 39}{space 1}   -0.14{col 48}{space 3}0.892{col 56}{space 4}-.0114292{col 69}{space 3} .0099561
{txt}{space 9}islam {c |}{col 16}{res}{space 2} .3608195{col 28}{space 2} .0290933{col 39}{space 1}   12.40{col 48}{space 3}0.000{col 56}{space 4} .3037075{col 69}{space 3} .4179315
{txt}{space 12}r2 {c |}{col 16}{res}{space 2}-.1294335{col 28}{space 2} .0631245{col 39}{space 1}   -2.05{col 48}{space 3}0.041{col 56}{space 4}-.2533511{col 69}{space 3}-.0055159
{txt}{space 12}r3 {c |}{col 16}{res}{space 2} .0696105{col 28}{space 2} .0705574{col 39}{space 1}    0.99{col 48}{space 3}0.324{col 56}{space 4}-.0688983{col 69}{space 3} .2081194
{txt}{space 12}r4 {c |}{col 16}{res}{space 2}-.0154901{col 28}{space 2} .0646913{col 39}{space 1}   -0.24{col 48}{space 3}0.811{col 56}{space 4}-.1424833{col 69}{space 3} .1115032
{txt}{space 12}r5 {c |}{col 16}{res}{space 2} .0531141{col 28}{space 2} .0866491{col 39}{space 1}    0.61{col 48}{space 3}0.540{col 56}{space 4}-.1169838{col 69}{space 3} .2232119
{txt}{space 12}r6 {c |}{col 16}{res}{space 2}-.0614949{col 28}{space 2} .0572924{col 39}{space 1}   -1.07{col 48}{space 3}0.283{col 56}{space 4}-.1739636{col 69}{space 3} .0509738
{txt}{space 12}r7 {c |}{col 16}{res}{space 2}-.0684969{col 28}{space 2} .0670533{col 39}{space 1}   -1.02{col 48}{space 3}0.307{col 56}{space 4} -.200127{col 69}{space 3} .0631332
{txt}{space 12}r8 {c |}{col 16}{res}{space 2}        0{col 28}{txt}  (omitted)
{space 9}_cons {c |}{col 16}{res}{space 2} .2385321{col 28}{space 2} .1090276{col 39}{space 1}    2.19{col 48}{space 3}0.029{col 56}{space 4} .0245038{col 69}{space 3} .4525604
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est12{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:781}"

{com}. esttab using "`drive'/HTEeducation.tex", replace ///
>         keep(education 1.t2 1.t2#c.education 1.t3 1.t3#c.education 1.t4 1.t4#c.education 1.t5 1.t5#c.education female age govt_emp income islam r2 r3 r4 r5 r6 r7 r8 _cons) ///
>         order(education 1.t2 1.t2#c.education 1.t3 1.t3#c.education 1.t4 1.t4#c.education 1.t5 1.t5#c.education female age govt_emp income islam r2 r3 r4 r5 r6 r7 r8 _cons) ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(sample i, label("Sample" "Observations")) ///
>         title("Treatment Effects Conditional on Education \label{c -(}tab:HTEeducation{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/HTEeducation.tex"'})

{com}. 
. 
. ******************************************************
. *** Table I6: Treatment Effects Conditional on Income
. ******************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std i.t2##c.income female age education govt_emp islam r2-r8 if treatment==1 | treatment==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,507
                                                {txt}F(14, 1492)       =  {res}    11.55
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0662
                                                {txt}Root MSE          =    {res} .39148

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t2 {c |}{col 14}{res}{space 2} .0658547{col 26}{space 2} .0602072{col 37}{space 1}    1.09{col 46}{space 3}0.274{col 54}{space 4} -.052245{col 67}{space 3} .1839545
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0063168{col 26}{space 2} .0053886{col 37}{space 1}    1.17{col 46}{space 3}0.241{col 54}{space 4}-.0042532{col 67}{space 3} .0168867
{txt}{space 12} {c |}
{space 1}t2#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0136987{col 26}{space 2} .0074522{col 37}{space 1}   -1.84{col 46}{space 3}0.066{col 54}{space 4}-.0283167{col 67}{space 3} .0009192
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0330912{col 26}{space 2} .0208872{col 37}{space 1}    1.58{col 46}{space 3}0.113{col 54}{space 4}-.0078802{col 67}{space 3} .0740627
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0024738{col 26}{space 2} .0008643{col 37}{space 1}   -2.86{col 46}{space 3}0.004{col 54}{space 4}-.0041693{col 67}{space 3}-.0007783
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.026451{col 26}{space 2} .0097406{col 37}{space 1}   -2.72{col 46}{space 3}0.007{col 54}{space 4}-.0455577{col 67}{space 3}-.0073443
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0075648{col 26}{space 2} .0269312{col 37}{space 1}   -0.28{col 46}{space 3}0.779{col 54}{space 4}-.0603919{col 67}{space 3} .0452623
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2737147{col 26}{space 2} .0280361{col 37}{space 1}    9.76{col 46}{space 3}0.000{col 54}{space 4} .2187205{col 67}{space 3}  .328709
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0629241{col 26}{space 2} .0452403{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-.1516655{col 67}{space 3} .0258174
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0535896{col 26}{space 2} .0516058{col 37}{space 1}    1.04{col 46}{space 3}0.299{col 54}{space 4}-.0476381{col 67}{space 3} .1548173
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0525094{col 26}{space 2}  .047968{col 37}{space 1}    1.09{col 46}{space 3}0.274{col 54}{space 4}-.0415824{col 67}{space 3} .1466012
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1069253{col 26}{space 2} .0601092{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0109822{col 67}{space 3} .2248328
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0252647{col 26}{space 2} .0424955{col 37}{space 1}    0.59{col 46}{space 3}0.552{col 54}{space 4}-.0580926{col 67}{space 3}  .108622
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0438348{col 26}{space 2} .0490733{col 37}{space 1}    0.89{col 46}{space 3}0.372{col 54}{space 4}-.0524252{col 67}{space 3} .1400948
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2941759{col 26}{space 2} .0773361{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} .1424768{col 67}{space 3}  .445875
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1507}"

{com}. eststo: reg o1_std i.t2##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==1 | treatment==2 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       752
                                                {txt}F(14, 737)        =  {res}     5.80
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0738
                                                {txt}Root MSE          =    {res} .38962

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t2 {c |}{col 14}{res}{space 2}-.0182612{col 26}{space 2} .0823123{col 37}{space 1}   -0.22{col 46}{space 3}0.824{col 54}{space 4}-.1798557{col 67}{space 3} .1433332
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0021585{col 26}{space 2} .0074916{col 37}{space 1}    0.29{col 46}{space 3}0.773{col 54}{space 4}-.0125489{col 67}{space 3}  .016866
{txt}{space 12} {c |}
{space 1}t2#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0085466{col 26}{space 2} .0104008{col 37}{space 1}   -0.82{col 46}{space 3}0.411{col 54}{space 4}-.0289654{col 67}{space 3} .0118722
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2}  .036166{col 26}{space 2} .0302405{col 37}{space 1}    1.20{col 46}{space 3}0.232{col 54}{space 4}-.0232017{col 67}{space 3} .0955337
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0030675{col 26}{space 2}  .001267{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.0055547{col 67}{space 3}-.0005802
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0227651{col 26}{space 2}  .013928{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.0501085{col 67}{space 3} .0045782
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0103374{col 26}{space 2} .0395382{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.0672834{col 67}{space 3} .0879583
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2597818{col 26}{space 2} .0410594{col 37}{space 1}    6.33{col 46}{space 3}0.000{col 54}{space 4} .1791745{col 67}{space 3} .3403892
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0014721{col 26}{space 2} .0626583{col 37}{space 1}   -0.02{col 46}{space 3}0.981{col 54}{space 4}-.1244821{col 67}{space 3} .1215379
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .1527716{col 26}{space 2} .0723992{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0106384{col 67}{space 3} .2949049
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0867597{col 26}{space 2}   .06679{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0443617{col 67}{space 3} .2178811
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1262041{col 26}{space 2} .0810017{col 37}{space 1}    1.56{col 46}{space 3}0.120{col 54}{space 4}-.0328175{col 67}{space 3} .2852256
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0685449{col 26}{space 2} .0590199{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.0473223{col 67}{space 3} .1844122
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0724746{col 26}{space 2} .0675633{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4}-.0601649{col 67}{space 3} .2051141
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2}   .30495{col 26}{space 2} .1096833{col 37}{space 1}    2.78{col 46}{space 3}0.006{col 54}{space 4} .0896211{col 67}{space 3}  .520279
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:752}"

{com}. eststo: reg o1_std i.t2##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==2 | treatment==2 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       755
                                                {txt}F(14, 740)        =  {res}     7.02
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0742
                                                {txt}Root MSE          =    {res} .39359

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t2 {c |}{col 14}{res}{space 2} .1442952{col 26}{space 2}  .089411{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4}-.0312343{col 67}{space 3} .3198246
{txt}{space 6}income {c |}{col 14}{res}{space 2}   .01032{col 26}{space 2} .0078468{col 37}{space 1}    1.32{col 46}{space 3}0.189{col 54}{space 4}-.0050845{col 67}{space 3} .0257246
{txt}{space 12} {c |}
{space 1}t2#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0181344{col 26}{space 2} .0109086{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4}-.0395499{col 67}{space 3} .0032811
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0313802{col 26}{space 2} .0291183{col 37}{space 1}    1.08{col 46}{space 3}0.282{col 54}{space 4}-.0257841{col 67}{space 3} .0885446
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0019513{col 26}{space 2} .0011851{col 37}{space 1}   -1.65{col 46}{space 3}0.100{col 54}{space 4}-.0042778{col 67}{space 3} .0003752
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.030963{col 26}{space 2} .0137319{col 37}{space 1}   -2.25{col 46}{space 3}0.024{col 54}{space 4}-.0579212{col 67}{space 3}-.0040048
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0159491{col 26}{space 2} .0377133{col 37}{space 1}   -0.42{col 46}{space 3}0.672{col 54}{space 4}-.0899869{col 67}{space 3} .0580886
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2828914{col 26}{space 2}  .038725{col 37}{space 1}    7.31{col 46}{space 3}0.000{col 54}{space 4} .2068674{col 67}{space 3} .3589154
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1232994{col 26}{space 2} .0660977{col 37}{space 1}   -1.87{col 46}{space 3}0.063{col 54}{space 4}-.2530608{col 67}{space 3}  .006462
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0570788{col 26}{space 2} .0739094{col 37}{space 1}   -0.77{col 46}{space 3}0.440{col 54}{space 4} -.202176{col 67}{space 3} .0880184
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0102667{col 26}{space 2} .0689642{col 37}{space 1}    0.15{col 46}{space 3}0.882{col 54}{space 4}-.1251222{col 67}{space 3} .1456555
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0805755{col 26}{space 2} .0885709{col 37}{space 1}    0.91{col 46}{space 3}0.363{col 54}{space 4}-.0933046{col 67}{space 3} .2544557
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0247362{col 26}{space 2} .0614902{col 37}{space 1}   -0.40{col 46}{space 3}0.688{col 54}{space 4}-.1454522{col 67}{space 3} .0959798
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0090995{col 26}{space 2} .0717548{col 37}{space 1}    0.13{col 46}{space 3}0.899{col 54}{space 4}-.1317677{col 67}{space 3} .1499668
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2971659{col 26}{space 2}   .10977{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .0816683{col 67}{space 3} .5126636
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:755}"

{com}. eststo: reg o1_std i.t3##c.income female age education govt_emp islam r2-r8 if treatment==1 | treatment==3, robust

{txt}Linear regression                               Number of obs     = {res}     1,536
                                                {txt}{help j_robustsingular:F(14, 1520) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0711
                                                {txt}Root MSE          =    {res} .38643

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t3 {c |}{col 14}{res}{space 2}  .053564{col 26}{space 2} .0586901{col 37}{space 1}    0.91{col 46}{space 3}0.362{col 54}{space 4}-.0615581{col 67}{space 3}  .168686
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0053513{col 26}{space 2} .0054077{col 37}{space 1}    0.99{col 46}{space 3}0.323{col 54}{space 4}-.0052562{col 67}{space 3} .0159587
{txt}{space 12} {c |}
{space 1}t3#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0062054{col 26}{space 2} .0072475{col 37}{space 1}   -0.86{col 46}{space 3}0.392{col 54}{space 4}-.0204216{col 67}{space 3} .0080108
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2}  .021144{col 26}{space 2} .0204813{col 37}{space 1}    1.03{col 46}{space 3}0.302{col 54}{space 4}-.0190306{col 67}{space 3} .0613187
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0016155{col 26}{space 2} .0008665{col 37}{space 1}   -1.86{col 46}{space 3}0.062{col 54}{space 4}-.0033152{col 67}{space 3} .0000842
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0287828{col 26}{space 2} .0094066{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.0472341{col 67}{space 3}-.0103316
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0126296{col 26}{space 2} .0265803{col 37}{space 1}   -0.48{col 46}{space 3}0.635{col 54}{space 4}-.0647676{col 67}{space 3} .0395083
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2795635{col 26}{space 2} .0276439{col 37}{space 1}   10.11{col 46}{space 3}0.000{col 54}{space 4} .2253392{col 67}{space 3} .3337877
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} -.144015{col 26}{space 2} .0301496{col 37}{space 1}   -4.78{col 46}{space 3}0.000{col 54}{space 4}-.2031543{col 67}{space 3}-.0848757
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} -.067715{col 26}{space 2} .0389931{col 37}{space 1}   -1.74{col 46}{space 3}0.083{col 54}{space 4} -.144201{col 67}{space 3} .0087709
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0119054{col 26}{space 2} .0326396{col 37}{space 1}    0.36{col 46}{space 3}0.715{col 54}{space 4}-.0521179{col 67}{space 3} .0759288
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0244057{col 26}{space 2} .0448775{col 37}{space 1}    0.54{col 46}{space 3}0.587{col 54}{space 4}-.0636228{col 67}{space 3} .1124341
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0675407{col 26}{space 2} .0240935{col 37}{space 1}   -2.80{col 46}{space 3}0.005{col 54}{space 4}-.1148008{col 67}{space 3}-.0202807
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0880659{col 26}{space 2} .0331578{col 37}{space 1}   -2.66{col 46}{space 3}0.008{col 54}{space 4}-.1531058{col 67}{space 3} -.023026
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0385876{col 26}{space 2} .0415374{col 37}{space 1}   -0.93{col 46}{space 3}0.353{col 54}{space 4}-.1200643{col 67}{space 3}  .042889
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3669391{col 26}{space 2} .0703794{col 37}{space 1}    5.21{col 46}{space 3}0.000{col 54}{space 4}  .228888{col 67}{space 3} .5049902
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1536}"

{com}. eststo: reg o1_std i.t3##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==1 | treatment==3 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       768
                                                {txt}F(14, 753)        =  {res}     5.21
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0729
                                                {txt}Root MSE          =    {res} .38998

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t3 {c |}{col 14}{res}{space 2} .0031552{col 26}{space 2} .0840547{col 37}{space 1}    0.04{col 46}{space 3}0.970{col 54}{space 4}-.1618542{col 67}{space 3} .1681646
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0029122{col 26}{space 2} .0075463{col 37}{space 1}    0.39{col 46}{space 3}0.700{col 54}{space 4} -.011902{col 67}{space 3} .0177264
{txt}{space 12} {c |}
{space 1}t3#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0013753{col 26}{space 2}  .010455{col 37}{space 1}   -0.13{col 46}{space 3}0.895{col 54}{space 4}-.0218997{col 67}{space 3} .0191491
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0145867{col 26}{space 2} .0296779{col 37}{space 1}    0.49{col 46}{space 3}0.623{col 54}{space 4}-.0436745{col 67}{space 3}  .072848
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0032843{col 26}{space 2} .0013026{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.0058416{col 67}{space 3}-.0007271
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0309646{col 26}{space 2} .0140062{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4}-.0584604{col 67}{space 3}-.0034688
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0065216{col 26}{space 2} .0416856{col 37}{space 1}   -0.16{col 46}{space 3}0.876{col 54}{space 4}-.0883555{col 67}{space 3} .0753123
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2855626{col 26}{space 2} .0417149{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .2036713{col 67}{space 3} .3674539
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0404968{col 26}{space 2} .0642358{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4}-.1665993{col 67}{space 3} .0856056
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}  .077236{col 26}{space 2} .0718482{col 37}{space 1}    1.07{col 46}{space 3}0.283{col 54}{space 4}-.0638107{col 67}{space 3} .2182826
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1044748{col 26}{space 2} .0666328{col 37}{space 1}    1.57{col 46}{space 3}0.117{col 54}{space 4}-.0263334{col 67}{space 3}  .235283
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0658341{col 26}{space 2} .0760017{col 37}{space 1}    0.87{col 46}{space 3}0.387{col 54}{space 4}-.0833662{col 67}{space 3} .2150345
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0517405{col 26}{space 2} .0593071{col 37}{space 1}    0.87{col 46}{space 3}0.383{col 54}{space 4}-.0646865{col 67}{space 3} .1681675
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}  .027702{col 26}{space 2} .0673344{col 37}{space 1}    0.41{col 46}{space 3}0.681{col 54}{space 4}-.1044835{col 67}{space 3} .1598875
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2}  .358834{col 26}{space 2} .1092857{col 37}{space 1}    3.28{col 46}{space 3}0.001{col 54}{space 4} .1442932{col 67}{space 3} .5733748
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:768}"

{com}. eststo: reg o1_std i.t3##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==2 | treatment==3 & order==2, robust

{txt}Linear regression                               Number of obs     = {res}       768
                                                {txt}{help j_robustsingular:F(14, 752) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0841
                                                {txt}Root MSE          =    {res} .38364

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t3 {c |}{col 14}{res}{space 2}  .106045{col 26}{space 2} .0826258{col 37}{space 1}    1.28{col 46}{space 3}0.200{col 54}{space 4}-.0561597{col 67}{space 3} .2682496
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0076756{col 26}{space 2} .0078026{col 37}{space 1}    0.98{col 46}{space 3}0.326{col 54}{space 4}-.0076419{col 67}{space 3} .0229931
{txt}{space 12} {c |}
{space 1}t3#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0112234{col 26}{space 2} .0101099{col 37}{space 1}   -1.11{col 46}{space 3}0.267{col 54}{space 4}-.0310703{col 67}{space 3} .0086236
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0272459{col 26}{space 2} .0284091{col 37}{space 1}    0.96{col 46}{space 3}0.338{col 54}{space 4}-.0285246{col 67}{space 3} .0830164
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0003017{col 26}{space 2}  .001145{col 37}{space 1}   -0.26{col 46}{space 3}0.792{col 54}{space 4}-.0025494{col 67}{space 3}  .001946
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0277586{col 26}{space 2} .0127407{col 37}{space 1}   -2.18{col 46}{space 3}0.030{col 54}{space 4}  -.05277{col 67}{space 3}-.0027471
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} -.014621{col 26}{space 2} .0350239{col 37}{space 1}   -0.42{col 46}{space 3}0.676{col 54}{space 4}-.0833773{col 67}{space 3} .0541352
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2684754{col 26}{space 2} .0371248{col 37}{space 1}    7.23{col 46}{space 3}0.000{col 54}{space 4} .1955949{col 67}{space 3} .3413558
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1550511{col 26}{space 2} .0429714{col 37}{space 1}   -3.61{col 46}{space 3}0.000{col 54}{space 4}-.2394093{col 67}{space 3} -.070693
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.1253393{col 26}{space 2} .0555516{col 37}{space 1}   -2.26{col 46}{space 3}0.024{col 54}{space 4}-.2343941{col 67}{space 3}-.0162846
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0155599{col 26}{space 2} .0451285{col 37}{space 1}    0.34{col 46}{space 3}0.730{col 54}{space 4}-.0730328{col 67}{space 3} .1041527
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0877034{col 26}{space 2} .0670768{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.0439766{col 67}{space 3} .2193834
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0956794{col 26}{space 2} .0338512{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4}-.1621335{col 67}{space 3}-.0292253
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.1057025{col 26}{space 2} .0460636{col 37}{space 1}   -2.29{col 46}{space 3}0.022{col 54}{space 4} -.196131{col 67}{space 3}-.0152739
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0088506{col 26}{space 2} .0579672{col 37}{space 1}    0.15{col 46}{space 3}0.879{col 54}{space 4}-.1049462{col 67}{space 3} .1226475
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3052532{col 26}{space 2} .0965024{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4}  .115807{col 67}{space 3} .4946994
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:768}"

{com}. eststo: reg o1_std i.t4##c.income female age education govt_emp islam r2-r8 if treatment==1 | treatment==4, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,521
                                                {txt}F(14, 1506)       =  {res}    15.49
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0842
                                                {txt}Root MSE          =    {res} .38668

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t4 {c |}{col 14}{res}{space 2} .0966998{col 26}{space 2}  .057706{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4}-.0164929{col 67}{space 3} .2098925
{txt}{space 6}income {c |}{col 14}{res}{space 2}   .00786{col 26}{space 2} .0053663{col 37}{space 1}    1.46{col 46}{space 3}0.143{col 54}{space 4}-.0026661{col 67}{space 3} .0183862
{txt}{space 12} {c |}
{space 1}t4#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0156976{col 26}{space 2} .0071739{col 37}{space 1}   -2.19{col 46}{space 3}0.029{col 54}{space 4}-.0297695{col 67}{space 3}-.0016257
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0243408{col 26}{space 2} .0204692{col 37}{space 1}    1.19{col 46}{space 3}0.235{col 54}{space 4}-.0158103{col 67}{space 3}  .064492
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0023243{col 26}{space 2} .0008777{col 37}{space 1}   -2.65{col 46}{space 3}0.008{col 54}{space 4} -.004046{col 67}{space 3}-.0006026
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0384973{col 26}{space 2} .0095519{col 37}{space 1}   -4.03{col 46}{space 3}0.000{col 54}{space 4}-.0572338{col 67}{space 3}-.0197608
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0056252{col 26}{space 2} .0269477{col 37}{space 1}   -0.21{col 46}{space 3}0.835{col 54}{space 4}-.0584843{col 67}{space 3} .0472338
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2909849{col 26}{space 2} .0261826{col 37}{space 1}   11.11{col 46}{space 3}0.000{col 54}{space 4} .2396266{col 67}{space 3} .3423432
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0753743{col 26}{space 2} .0480925{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-.1697096{col 67}{space 3} .0189611
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0401785{col 26}{space 2} .0519996{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4}-.0618209{col 67}{space 3} .1421779
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0336598{col 26}{space 2} .0494157{col 37}{space 1}    0.68{col 46}{space 3}0.496{col 54}{space 4}-.0632711{col 67}{space 3} .1305907
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0767226{col 26}{space 2} .0565308{col 37}{space 1}    1.36{col 46}{space 3}0.175{col 54}{space 4}-.0341648{col 67}{space 3} .1876101
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0146611{col 26}{space 2} .0439537{col 37}{space 1}   -0.33{col 46}{space 3}0.739{col 54}{space 4} -.100878{col 67}{space 3} .0715558
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0066428{col 26}{space 2}  .049649{col 37}{space 1}   -0.13{col 46}{space 3}0.894{col 54}{space 4}-.1040313{col 67}{space 3} .0907457
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .3444182{col 26}{space 2}  .078877{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} .1896978{col 67}{space 3} .4991386
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1521}"

{com}. eststo: reg o1_std i.t4##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==1 | treatment==4 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       762
                                                {txt}F(14, 747)        =  {res}     8.89
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1090
                                                {txt}Root MSE          =    {res} .38358

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t4 {c |}{col 14}{res}{space 2} .0312531{col 26}{space 2} .0781305{col 37}{space 1}    0.40{col 46}{space 3}0.689{col 54}{space 4}-.1221284{col 67}{space 3} .1846345
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0056914{col 26}{space 2} .0074545{col 37}{space 1}    0.76{col 46}{space 3}0.445{col 54}{space 4} -.008943{col 67}{space 3} .0203257
{txt}{space 12} {c |}
{space 1}t4#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0121148{col 26}{space 2}  .009946{col 37}{space 1}   -1.22{col 46}{space 3}0.224{col 54}{space 4}-.0316402{col 67}{space 3} .0074106
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0499015{col 26}{space 2} .0291797{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0073825{col 67}{space 3} .1071855
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0044502{col 26}{space 2} .0012422{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0068889{col 67}{space 3}-.0020116
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0407412{col 26}{space 2} .0136425{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4}-.0675235{col 67}{space 3}-.0139589
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0296811{col 26}{space 2} .0394932{col 37}{space 1}   -0.75{col 46}{space 3}0.453{col 54}{space 4} -.107212{col 67}{space 3} .0478497
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3035766{col 26}{space 2} .0390956{col 37}{space 1}    7.76{col 46}{space 3}0.000{col 54}{space 4} .2268263{col 67}{space 3} .3803269
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0064108{col 26}{space 2} .0677247{col 37}{space 1}    0.09{col 46}{space 3}0.925{col 54}{space 4}-.1265426{col 67}{space 3} .1393641
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .1425204{col 26}{space 2} .0737534{col 37}{space 1}    1.93{col 46}{space 3}0.054{col 54}{space 4}-.0022683{col 67}{space 3}  .287309
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1198537{col 26}{space 2} .0696096{col 37}{space 1}    1.72{col 46}{space 3}0.086{col 54}{space 4}   -.0168{col 67}{space 3} .2565074
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .2078586{col 26}{space 2} .0738595{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0628616{col 67}{space 3} .3528556
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0712543{col 26}{space 2} .0615361{col 37}{space 1}    1.16{col 46}{space 3}0.247{col 54}{space 4}-.0495499{col 67}{space 3} .1920586
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0884473{col 26}{space 2} .0699267{col 37}{space 1}    1.26{col 46}{space 3}0.206{col 54}{space 4} -.048829{col 67}{space 3} .2257236
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .3571372{col 26}{space 2} .1111764{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .1388819{col 67}{space 3} .5753925
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est8{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:762}"

{com}. eststo: reg o1_std i.t4##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==2 | treatment==4 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       759
                                                {txt}F(14, 744)        =  {res}     8.59
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0826
                                                {txt}Root MSE          =    {res} .38855

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t4 {c |}{col 14}{res}{space 2} .1790578{col 26}{space 2} .0854176{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0113696{col 67}{space 3}  .346746
{txt}{space 6}income {c |}{col 14}{res}{space 2}  .010737{col 26}{space 2} .0078112{col 37}{space 1}    1.37{col 46}{space 3}0.170{col 54}{space 4}-.0045976{col 67}{space 3} .0260716
{txt}{space 12} {c |}
{space 1}t4#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0212081{col 26}{space 2} .0104236{col 37}{space 1}   -2.03{col 46}{space 3}0.042{col 54}{space 4}-.0416713{col 67}{space 3}-.0007449
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2}-.0016587{col 26}{space 2} .0289117{col 37}{space 1}   -0.06{col 46}{space 3}0.954{col 54}{space 4} -.058417{col 67}{space 3} .0550995
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0006099{col 26}{space 2} .0012262{col 37}{space 1}   -0.50{col 46}{space 3}0.619{col 54}{space 4}-.0030171{col 67}{space 3} .0017972
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0368368{col 26}{space 2}  .013525{col 37}{space 1}   -2.72{col 46}{space 3}0.007{col 54}{space 4}-.0633886{col 67}{space 3} -.010285
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0226044{col 26}{space 2} .0373825{col 37}{space 1}    0.60{col 46}{space 3}0.546{col 54}{space 4}-.0507833{col 67}{space 3} .0959922
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .2895235{col 26}{space 2} .0356552{col 37}{space 1}    8.12{col 46}{space 3}0.000{col 54}{space 4} .2195268{col 67}{space 3} .3595203
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1483143{col 26}{space 2}  .067549{col 37}{space 1}   -2.20{col 46}{space 3}0.028{col 54}{space 4}-.2809236{col 67}{space 3}-.0157049
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0609356{col 26}{space 2} .0717893{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-.2018693{col 67}{space 3} .0799982
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0466451{col 26}{space 2} .0688485{col 37}{space 1}   -0.68{col 46}{space 3}0.498{col 54}{space 4}-.1818056{col 67}{space 3} .0885155
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0802459{col 26}{space 2}  .088553{col 37}{space 1}   -0.91{col 46}{space 3}0.365{col 54}{space 4}-.2540894{col 67}{space 3} .0935975
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0957558{col 26}{space 2} .0615065{col 37}{space 1}   -1.56{col 46}{space 3}0.120{col 54}{space 4}-.2165028{col 67}{space 3} .0249912
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0950867{col 26}{space 2}  .069834{col 37}{space 1}   -1.36{col 46}{space 3}0.174{col 54}{space 4}-.2321818{col 67}{space 3} .0420085
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .3278304{col 26}{space 2}  .111909{col 37}{space 1}    2.93{col 46}{space 3}0.003{col 54}{space 4} .1081354{col 67}{space 3} .5475254
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est9{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:759}"

{com}. eststo: reg o1_std i.t5##c.income female age education govt_emp islam r2-r8 if treatment==1 | treatment==5, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,537
                                                {txt}F(14, 1522)       =  {res}    19.29
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0897
                                                {txt}Root MSE          =    {res} .38802

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t5 {c |}{col 14}{res}{space 2}  .135002{col 26}{space 2} .0586522{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0199543{col 67}{space 3} .2500496
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0057817{col 26}{space 2} .0053758{col 37}{space 1}    1.08{col 46}{space 3}0.282{col 54}{space 4}-.0047631{col 67}{space 3} .0163265
{txt}{space 12} {c |}
{space 1}t5#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0170665{col 26}{space 2} .0072125{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-.0312139{col 67}{space 3}-.0029191
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0496724{col 26}{space 2} .0205032{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0094549{col 67}{space 3} .0898899
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0016675{col 26}{space 2}  .000874{col 37}{space 1}   -1.91{col 46}{space 3}0.057{col 54}{space 4}-.0033819{col 67}{space 3}  .000047
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0176173{col 26}{space 2} .0096202{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-.0364876{col 67}{space 3}  .001253
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0204064{col 26}{space 2}  .027356{col 37}{space 1}    0.75{col 46}{space 3}0.456{col 54}{space 4} -.033253{col 67}{space 3} .0740657
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3306701{col 26}{space 2} .0244749{col 37}{space 1}   13.51{col 46}{space 3}0.000{col 54}{space 4}  .282662{col 67}{space 3} .3786781
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0833166{col 26}{space 2} .0450301{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-.1716442{col 67}{space 3}  .005011
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0669395{col 26}{space 2} .0509004{col 37}{space 1}    1.32{col 46}{space 3}0.189{col 54}{space 4}-.0329028{col 67}{space 3} .1667817
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0523169{col 26}{space 2} .0473278{col 37}{space 1}    1.11{col 46}{space 3}0.269{col 54}{space 4}-.0405177{col 67}{space 3} .1451516
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1275379{col 26}{space 2} .0579728{col 37}{space 1}    2.20{col 46}{space 3}0.028{col 54}{space 4} .0138229{col 67}{space 3} .2412529
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0026717{col 26}{space 2} .0415872{col 37}{space 1}   -0.06{col 46}{space 3}0.949{col 54}{space 4} -.084246{col 67}{space 3} .0789027
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0023113{col 26}{space 2} .0475235{col 37}{space 1}   -0.05{col 46}{space 3}0.961{col 54}{space 4}-.0955297{col 67}{space 3} .0909071
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .1818568{col 26}{space 2} .0749548{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .0348311{col 67}{space 3} .3288825
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est10{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1537}"

{com}. eststo: reg o1_std i.t5##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==1 | treatment==5 & order==1, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       756
                                                {txt}F(14, 741)        =  {res}     6.60
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0831
                                                {txt}Root MSE          =    {res} .39165

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t5 {c |}{col 14}{res}{space 2} .1072306{col 26}{space 2} .0825831{col 37}{space 1}    1.30{col 46}{space 3}0.195{col 54}{space 4}-.0548941{col 67}{space 3} .2693554
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0019886{col 26}{space 2} .0076136{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.0129582{col 67}{space 3} .0169353
{txt}{space 12} {c |}
{space 1}t5#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0140023{col 26}{space 2} .0103459{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-.0343132{col 67}{space 3} .0063085
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0346639{col 26}{space 2} .0297861{col 37}{space 1}    1.16{col 46}{space 3}0.245{col 54}{space 4}-.0238113{col 67}{space 3} .0931391
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0027059{col 26}{space 2} .0012876{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.0052337{col 67}{space 3}-.0001782
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0191419{col 26}{space 2} .0139622{col 37}{space 1}   -1.37{col 46}{space 3}0.171{col 54}{space 4} -.046552{col 67}{space 3} .0082683
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0224682{col 26}{space 2} .0410087{col 37}{space 1}   -0.55{col 46}{space 3}0.584{col 54}{space 4}-.1029753{col 67}{space 3} .0580388
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3022256{col 26}{space 2} .0436189{col 37}{space 1}    6.93{col 46}{space 3}0.000{col 54}{space 4} .2165943{col 67}{space 3}  .387857
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0412157{col 26}{space 2} .0650181{col 37}{space 1}   -0.63{col 46}{space 3}0.526{col 54}{space 4}-.1688574{col 67}{space 3}  .086426
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0799384{col 26}{space 2} .0732448{col 37}{space 1}    1.09{col 46}{space 3}0.275{col 54}{space 4}-.0638537{col 67}{space 3} .2237304
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .1314591{col 26}{space 2} .0698825{col 37}{space 1}    1.88{col 46}{space 3}0.060{col 54}{space 4}-.0057321{col 67}{space 3} .2686503
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1985862{col 26}{space 2}   .07976{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} .0420037{col 67}{space 3} .3551687
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0601414{col 26}{space 2} .0610334{col 37}{space 1}    0.99{col 46}{space 3}0.325{col 54}{space 4}-.0596776{col 67}{space 3} .1799605
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0615843{col 26}{space 2} .0682163{col 37}{space 1}    0.90{col 46}{space 3}0.367{col 54}{space 4} -.072336{col 67}{space 3} .1955046
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2525035{col 26}{space 2} .1116536{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .0333085{col 67}{space 3} .4716986
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est11{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:756}"

{com}. eststo: reg o1_std i.t5##c.income female age education govt_emp islam r2-r8 if treatment==1 & order==2 | treatment==5 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       781
                                                {txt}F(14, 766)        =  {res}    16.41
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1077
                                                {txt}Root MSE          =    {res} .38556

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}1.t5 {c |}{col 14}{res}{space 2} .1588087{col 26}{space 2} .0846738{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4}-.0074115{col 67}{space 3} .3250289
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0094185{col 26}{space 2} .0077703{col 37}{space 1}    1.21{col 46}{space 3}0.226{col 54}{space 4}-.0058352{col 67}{space 3} .0246722
{txt}{space 12} {c |}
{space 1}t5#c.income {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0194971{col 26}{space 2} .0102676{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4}-.0396532{col 67}{space 3} .0006589
{txt}{space 12} {c |}
{space 6}female {c |}{col 14}{res}{space 2} .0664395{col 26}{space 2}  .028375{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .0107374{col 67}{space 3} .1221416
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0007886{col 26}{space 2} .0011888{col 37}{space 1}   -0.66{col 46}{space 3}0.507{col 54}{space 4}-.0031224{col 67}{space 3} .0015452
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0153873{col 26}{space 2} .0134143{col 37}{space 1}   -1.15{col 46}{space 3}0.252{col 54}{space 4}-.0417205{col 67}{space 3} .0109459
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}  .058545{col 26}{space 2} .0367125{col 37}{space 1}    1.59{col 46}{space 3}0.111{col 54}{space 4}-.0135241{col 67}{space 3} .1306141
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .3559541{col 26}{space 2} .0286233{col 37}{space 1}   12.44{col 46}{space 3}0.000{col 54}{space 4} .2997646{col 67}{space 3} .4121435
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1225596{col 26}{space 2} .0631045{col 37}{space 1}   -1.94{col 46}{space 3}0.052{col 54}{space 4} -.246438{col 67}{space 3} .0013187
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0734362{col 26}{space 2} .0708863{col 37}{space 1}    1.04{col 46}{space 3}0.301{col 54}{space 4}-.0657182{col 67}{space 3} .2125907
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0143322{col 26}{space 2} .0645354{col 37}{space 1}   -0.22{col 46}{space 3}0.824{col 54}{space 4}-.1410194{col 67}{space 3} .1123551
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0548208{col 26}{space 2} .0859165{col 37}{space 1}    0.64{col 46}{space 3}0.524{col 54}{space 4}-.1138388{col 67}{space 3} .2234805
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0570924{col 26}{space 2} .0571413{col 37}{space 1}   -1.00{col 46}{space 3}0.318{col 54}{space 4}-.1692645{col 67}{space 3} .0550797
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} -.060319{col 26}{space 2} .0667476{col 37}{space 1}   -0.90{col 46}{space 3}0.366{col 54}{space 4} -.191349{col 67}{space 3}  .070711
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .1092446{col 26}{space 2} .1041073{col 37}{space 1}    1.05{col 46}{space 3}0.294{col 54}{space 4} -.095125{col 67}{space 3} .3136142
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est12{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:781}"

{com}. esttab using "`drive'/HTEincome.tex", replace ///
>         keep(income 1.t2 1.t2#c.income 1.t3 1.t3#c.income 1.t4 1.t4#c.income 1.t5 1.t5#c.income female age govt_emp education islam r2 r3 r4 r5 r6 r7 r8 _cons) ///
>         order(income 1.t2 1.t2#c.income 1.t3 1.t3#c.income 1.t4 1.t4#c.income 1.t5 1.t5#c.income female age govt_emp education islam r2 r3 r4 r5 r6 r7 r8 _cons) ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(sample i, label("Sample" "Observations")) ///
>         title("Treatment Effects Conditional on Income \label{c -(}tab:HTEincome{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/HTEincome.tex"'})

{com}. 
. 
. 
. 
. ************************************************************
. *** Table I7: Treatment Effects Conditional on Partisanship
. ************************************************************
. est clear
{res}{txt}
{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==1, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,585
                                                {txt}F(16, 1568)       =  {res}     2.74
                                                {txt}Prob > F          = {res}    0.0002
                                                {txt}R-squared         = {res}    0.0261
                                                {txt}Root MSE          =    {res} .33731

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0806968{col 26}{space 2} .0275648{col 37}{space 1}   -2.93{col 46}{space 3}0.003{col 54}{space 4}-.1347646{col 67}{space 3} -.026629
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0295449{col 26}{space 2} .0261151{col 37}{space 1}   -1.13{col 46}{space 3}0.258{col 54}{space 4}-.0807691{col 67}{space 3} .0216792
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0013844{col 26}{space 2} .0257061{col 37}{space 1}   -0.05{col 46}{space 3}0.957{col 54}{space 4}-.0518063{col 67}{space 3} .0490375
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0020452{col 26}{space 2} .0261566{col 37}{space 1}   -0.08{col 46}{space 3}0.938{col 54}{space 4}-.0533507{col 67}{space 3} .0492603
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0325775{col 26}{space 2} .0182822{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0032826{col 67}{space 3} .0684376
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0010079{col 26}{space 2} .0008491{col 37}{space 1}    1.19{col 46}{space 3}0.235{col 54}{space 4}-.0006576{col 67}{space 3} .0026733
{txt}{space 3}education {c |}{col 14}{res}{space 2} -.013334{col 26}{space 2} .0076022{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}-.0282457{col 67}{space 3} .0015776
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0077341{col 26}{space 2}  .024102{col 37}{space 1}    0.32{col 46}{space 3}0.748{col 54}{space 4}-.0395415{col 67}{space 3} .0550096
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0011883{col 26}{space 2} .0034362{col 37}{space 1}   -0.35{col 46}{space 3}0.730{col 54}{space 4}-.0079284{col 67}{space 3} .0055517
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0815711{col 26}{space 2} .0780264{col 37}{space 1}    1.05{col 46}{space 3}0.296{col 54}{space 4}-.0714759{col 67}{space 3} .2346181
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0947539{col 26}{space 2} .0424574{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-.1780331{col 67}{space 3}-.0114746
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0532765{col 26}{space 2} .0396655{col 37}{space 1}    1.34{col 46}{space 3}0.179{col 54}{space 4}-.0245266{col 67}{space 3} .1310796
{txt}{space 10}r4 {c |}{col 14}{res}{space 2} .0064118{col 26}{space 2} .0376523{col 37}{space 1}    0.17{col 46}{space 3}0.865{col 54}{space 4}-.0674424{col 67}{space 3} .0802659
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0186887{col 26}{space 2}  .034334{col 37}{space 1}   -0.54{col 46}{space 3}0.586{col 54}{space 4}-.0860341{col 67}{space 3} .0486568
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} -.032506{col 26}{space 2} .0399125{col 37}{space 1}   -0.81{col 46}{space 3}0.416{col 54}{space 4}-.1107934{col 67}{space 3} .0457815
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0190759{col 26}{space 2} .0425049{col 37}{space 1}   -0.45{col 46}{space 3}0.654{col 54}{space 4}-.1024483{col 67}{space 3} .0642964
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6752114{col 26}{space 2}  .094069{col 37}{space 1}    7.18{col 46}{space 3}0.000{col 54}{space 4} .4906971{col 67}{space 3} .8597256
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1585}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Erdoğan"

{txt}added macro:
             e(middle) : "{res:Erdoğan}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     1,233
                                                {txt}F(16, 1216)       =  {res}     4.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0529
                                                {txt}Root MSE          =    {res} .24009

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0483316{col 26}{space 2} .0202495{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.0880595{col 67}{space 3}-.0086038
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .050076{col 26}{space 2} .0237703{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0034406{col 67}{space 3} .0967114
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0246498{col 26}{space 2} .0200215{col 37}{space 1}   -1.23{col 46}{space 3}0.218{col 54}{space 4}-.0639303{col 67}{space 3} .0146307
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0090214{col 26}{space 2} .0219674{col 37}{space 1}    0.41{col 46}{space 3}0.681{col 54}{space 4}-.0340769{col 67}{space 3} .0521197
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0020058{col 26}{space 2} .0140221{col 37}{space 1}   -0.14{col 46}{space 3}0.886{col 54}{space 4} -.029516{col 67}{space 3} .0255044
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0020409{col 26}{space 2} .0005693{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.0031578{col 67}{space 3}-.0009239
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0160047{col 26}{space 2} .0076569{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-.0310269{col 67}{space 3}-.0009825
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}  .021013{col 26}{space 2} .0208277{col 37}{space 1}    1.01{col 46}{space 3}0.313{col 54}{space 4}-.0198493{col 67}{space 3} .0618752
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0014914{col 26}{space 2} .0028615{col 37}{space 1}   -0.52{col 46}{space 3}0.602{col 54}{space 4}-.0071055{col 67}{space 3} .0041226
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0561014{col 26}{space 2} .0162157{col 37}{space 1}    3.46{col 46}{space 3}0.001{col 54}{space 4} .0242875{col 67}{space 3} .0879152
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0400946{col 26}{space 2} .0349397{col 37}{space 1}    1.15{col 46}{space 3}0.251{col 54}{space 4}-.0284542{col 67}{space 3} .1086434
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0107351{col 26}{space 2}  .039043{col 37}{space 1}    0.27{col 46}{space 3}0.783{col 54}{space 4} -.065864{col 67}{space 3} .0873341
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0078122{col 26}{space 2} .0343979{col 37}{space 1}   -0.23{col 46}{space 3}0.820{col 54}{space 4}-.0752979{col 67}{space 3} .0596736
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}  .063524{col 26}{space 2} .0493553{col 37}{space 1}    1.29{col 46}{space 3}0.198{col 54}{space 4} -.033307{col 67}{space 3} .1603551
{txt}{space 10}r6 {c |}{col 14}{res}{space 2} .0070072{col 26}{space 2}  .032079{col 37}{space 1}    0.22{col 46}{space 3}0.827{col 54}{space 4}-.0559292{col 67}{space 3} .0699436
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}  .013346{col 26}{space 2}  .037102{col 37}{space 1}    0.36{col 46}{space 3}0.719{col 54}{space 4} -.059445{col 67}{space 3}  .086137
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2254522{col 26}{space 2} .0518181{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4} .1237893{col 67}{space 3}  .327115
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1233}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Opposition"

{txt}added macro:
             e(middle) : "{res:Opposition}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==3, robust

{txt}Linear regression                               Number of obs     = {res}     1,021
                                                {txt}{help j_robustsingular:F(16, 1003) }      =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0490
                                                {txt}Root MSE          =    {res} .32542

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0336411{col 26}{space 2} .0322959{col 37}{space 1}    1.04{col 46}{space 3}0.298{col 54}{space 4}-.0297342{col 67}{space 3} .0970164
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0627474{col 26}{space 2} .0326404{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4} -.001304{col 67}{space 3} .1267988
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0260185{col 26}{space 2} .0321863{col 37}{space 1}    0.81{col 46}{space 3}0.419{col 54}{space 4}-.0371417{col 67}{space 3} .0891786
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0454488{col 26}{space 2} .0315888{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4} -.016539{col 67}{space 3} .1074365
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0078321{col 26}{space 2}  .021253{col 37}{space 1}   -0.37{col 46}{space 3}0.713{col 54}{space 4}-.0495376{col 67}{space 3} .0338734
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0019013{col 26}{space 2} .0008279{col 37}{space 1}   -2.30{col 46}{space 3}0.022{col 54}{space 4}-.0035258{col 67}{space 3}-.0002767
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0011928{col 26}{space 2} .0098079{col 37}{space 1}    0.12{col 46}{space 3}0.903{col 54}{space 4}-.0180535{col 67}{space 3} .0204392
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0483058{col 26}{space 2} .0248682{col 37}{space 1}   -1.94{col 46}{space 3}0.052{col 54}{space 4}-.0971054{col 67}{space 3} .0004939
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0105415{col 26}{space 2} .0039866{col 37}{space 1}   -2.64{col 46}{space 3}0.008{col 54}{space 4}-.0183645{col 67}{space 3}-.0027185
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1489054{col 26}{space 2} .0307597{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4} .0885447{col 67}{space 3} .2092662
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1590882{col 26}{space 2} .0354598{col 37}{space 1}   -4.49{col 46}{space 3}0.000{col 54}{space 4}-.2286721{col 67}{space 3}-.0895043
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} -.190457{col 26}{space 2} .0414952{col 37}{space 1}   -4.59{col 46}{space 3}0.000{col 54}{space 4}-.2718844{col 67}{space 3}-.1090297
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.1488178{col 26}{space 2} .0413406{col 37}{space 1}   -3.60{col 46}{space 3}0.000{col 54}{space 4}-.2299417{col 67}{space 3}-.0676939
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0795151{col 26}{space 2} .0501605{col 37}{space 1}   -1.59{col 46}{space 3}0.113{col 54}{space 4}-.1779467{col 67}{space 3} .0189165
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.1432943{col 26}{space 2} .0315328{col 37}{space 1}   -4.54{col 46}{space 3}0.000{col 54}{space 4}-.2051721{col 67}{space 3}-.0814164
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.1085215{col 26}{space 2} .0400707{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4}-.1871536{col 67}{space 3}-.0298894
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.1295239{col 26}{space 2} .0485919{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.2248774{col 67}{space 3}-.0341705
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4342941{col 26}{space 2} .0724863{col 37}{space 1}    5.99{col 46}{space 3}0.000{col 54}{space 4}  .292052{col 67}{space 3} .5765362
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. estadd local sample "Full", replace

{txt}added macro:
             e(sample) : "{res:Full}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:1021}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Unaffiliated"

{txt}added macro:
             e(middle) : "{res:Unaffiliated}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==1 & order==2, robust
{txt}{p 0 6 2}note: {bf:r3} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       805
                                                {txt}F(16, 788)        =  {res}     1.35
                                                {txt}Prob > F          = {res}    0.1613
                                                {txt}R-squared         = {res}    0.0252
                                                {txt}Root MSE          =    {res} .33309

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0477457{col 26}{space 2} .0386793{col 37}{space 1}   -1.23{col 46}{space 3}0.217{col 54}{space 4}-.1236723{col 67}{space 3} .0281809
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0111907{col 26}{space 2} .0374338{col 37}{space 1}   -0.30{col 46}{space 3}0.765{col 54}{space 4}-.0846724{col 67}{space 3}  .062291
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0051368{col 26}{space 2} .0361646{col 37}{space 1}    0.14{col 46}{space 3}0.887{col 54}{space 4}-.0658534{col 67}{space 3} .0761271
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0017528{col 26}{space 2} .0381292{col 37}{space 1}   -0.05{col 46}{space 3}0.963{col 54}{space 4}-.0765996{col 67}{space 3}  .073094
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0205353{col 26}{space 2} .0250072{col 37}{space 1}    0.82{col 46}{space 3}0.412{col 54}{space 4}-.0285533{col 67}{space 3} .0696238
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0017077{col 26}{space 2} .0011067{col 37}{space 1}    1.54{col 46}{space 3}0.123{col 54}{space 4}-.0004648{col 67}{space 3} .0038801
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0074021{col 26}{space 2} .0110219{col 37}{space 1}   -0.67{col 46}{space 3}0.502{col 54}{space 4}-.0290378{col 67}{space 3} .0142335
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0004492{col 26}{space 2} .0326488{col 37}{space 1}   -0.01{col 46}{space 3}0.989{col 54}{space 4}-.0645382{col 67}{space 3} .0636397
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0038587{col 26}{space 2} .0048678{col 37}{space 1}   -0.79{col 46}{space 3}0.428{col 54}{space 4}-.0134141{col 67}{space 3} .0056966
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0963437{col 26}{space 2} .1014123{col 37}{space 1}    0.95{col 46}{space 3}0.342{col 54}{space 4}-.1027266{col 67}{space 3}  .295414
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1207317{col 26}{space 2} .0531061{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.2249779{col 67}{space 3}-.0164855
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r4 {c |}{col 14}{res}{space 2} -.006739{col 26}{space 2} .0452107{col 37}{space 1}   -0.15{col 46}{space 3}0.882{col 54}{space 4}-.0954868{col 67}{space 3} .0820087
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0044381{col 26}{space 2} .0616424{col 37}{space 1}   -0.07{col 46}{space 3}0.943{col 54}{space 4}-.1254407{col 67}{space 3} .1165646
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0598259{col 26}{space 2} .0407212{col 37}{space 1}   -1.47{col 46}{space 3}0.142{col 54}{space 4}-.1397608{col 67}{space 3}  .020109
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0980312{col 26}{space 2} .0502619{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-.1966943{col 67}{space 3} .0006319
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0632358{col 26}{space 2} .0521618{col 37}{space 1}   -1.21{col 46}{space 3}0.226{col 54}{space 4}-.1656284{col 67}{space 3} .0391568
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6681667{col 26}{space 2} .1181711{col 37}{space 1}    5.65{col 46}{space 3}0.000{col 54}{space 4} .4361993{col 67}{space 3} .9001341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:805}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Erdoğan"

{txt}added macro:
             e(middle) : "{res:Erdoğan}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==2 & order==2, robust
{txt}{p 0 6 2}note: {bf:r8} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       615
                                                {txt}F(16, 598)        =  {res}     2.63
                                                {txt}Prob > F          = {res}    0.0005
                                                {txt}R-squared         = {res}    0.0494
                                                {txt}Root MSE          =    {res} .24287

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0431066{col 26}{space 2} .0325969{col 37}{space 1}   -1.32{col 46}{space 3}0.187{col 54}{space 4}-.1071249{col 67}{space 3} .0209116
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .030898{col 26}{space 2} .0341781{col 37}{space 1}    0.90{col 46}{space 3}0.366{col 54}{space 4}-.0362257{col 67}{space 3} .0980216
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0436931{col 26}{space 2} .0302024{col 37}{space 1}   -1.45{col 46}{space 3}0.149{col 54}{space 4}-.1030086{col 67}{space 3} .0156225
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0221939{col 26}{space 2} .0311821{col 37}{space 1}   -0.71{col 46}{space 3}0.477{col 54}{space 4}-.0834337{col 67}{space 3} .0390459
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0290119{col 26}{space 2} .0203094{col 37}{space 1}    1.43{col 46}{space 3}0.154{col 54}{space 4}-.0108745{col 67}{space 3} .0688984
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0016098{col 26}{space 2} .0007555{col 37}{space 1}   -2.13{col 46}{space 3}0.034{col 54}{space 4}-.0030935{col 67}{space 3} -.000126
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0169716{col 26}{space 2} .0114188{col 37}{space 1}   -1.49{col 46}{space 3}0.138{col 54}{space 4}-.0393975{col 67}{space 3} .0054542
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0150261{col 26}{space 2}  .030954{col 37}{space 1}    0.49{col 46}{space 3}0.628{col 54}{space 4}-.0457656{col 67}{space 3} .0758178
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0000589{col 26}{space 2} .0041082{col 37}{space 1}   -0.01{col 46}{space 3}0.989{col 54}{space 4}-.0081272{col 67}{space 3} .0080093
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0488895{col 26}{space 2}  .021933{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0058145{col 67}{space 3} .0919645
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}  .023839{col 26}{space 2} .0573525{col 37}{space 1}    0.42{col 46}{space 3}0.678{col 54}{space 4}-.0887977{col 67}{space 3} .1364757
{txt}{space 10}r3 {c |}{col 14}{res}{space 2} .0195811{col 26}{space 2} .0650066{col 37}{space 1}    0.30{col 46}{space 3}0.763{col 54}{space 4}-.1080879{col 67}{space 3} .1472501
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0211543{col 26}{space 2}  .056235{col 37}{space 1}   -0.38{col 46}{space 3}0.707{col 54}{space 4}-.1315964{col 67}{space 3} .0892878
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .0486855{col 26}{space 2} .0714873{col 37}{space 1}    0.68{col 46}{space 3}0.496{col 54}{space 4}-.0917112{col 67}{space 3} .1890822
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0113988{col 26}{space 2} .0529504{col 37}{space 1}   -0.22{col 46}{space 3}0.830{col 54}{space 4}-.1153903{col 67}{space 3} .0925926
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .0096657{col 26}{space 2} .0601372{col 37}{space 1}    0.16{col 46}{space 3}0.872{col 54}{space 4}-.1084402{col 67}{space 3} .1277716
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .2199064{col 26}{space 2} .0726936{col 37}{space 1}    3.03{col 46}{space 3}0.003{col 54}{space 4} .0771407{col 67}{space 3} .3626722
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est5{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:615}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Opposition"

{txt}added macro:
             e(middle) : "{res:Opposition}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==3 & order==2, robust

{txt}Linear regression                               Number of obs     = {res}       518
                                                {txt}{help j_robustsingular:F(16, 500) }       =  {res}        .
                                                {txt}Prob > F          = {res}         .
                                                {txt}R-squared         = {res}    0.0665
                                                {txt}Root MSE          =    {res} .32803

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0583244{col 26}{space 2} .0440897{col 37}{space 1}    1.32{col 46}{space 3}0.186{col 54}{space 4}-.0282995{col 67}{space 3} .1449483
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .1095617{col 26}{space 2} .0453828{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0203972{col 67}{space 3} .1987262
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} .0830144{col 26}{space 2} .0459107{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0071873{col 67}{space 3} .1732161
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0757515{col 26}{space 2}  .042746{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}-.0082325{col 67}{space 3} .1597355
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0075952{col 26}{space 2} .0297397{col 37}{space 1}   -0.26{col 46}{space 3}0.799{col 54}{space 4}-.0660253{col 67}{space 3} .0508349
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0010602{col 26}{space 2} .0011287{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-.0032778{col 67}{space 3} .0011573
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0020049{col 26}{space 2}  .013567{col 37}{space 1}   -0.15{col 46}{space 3}0.883{col 54}{space 4}-.0286602{col 67}{space 3} .0246504
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0349885{col 26}{space 2} .0345721{col 37}{space 1}   -1.01{col 46}{space 3}0.312{col 54}{space 4}-.1029129{col 67}{space 3} .0329359
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0120608{col 26}{space 2} .0053807{col 37}{space 1}   -2.24{col 46}{space 3}0.025{col 54}{space 4}-.0226323{col 67}{space 3}-.0014893
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1703312{col 26}{space 2} .0414829{col 37}{space 1}    4.11{col 46}{space 3}0.000{col 54}{space 4} .0888289{col 67}{space 3} .2518336
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} -.154993{col 26}{space 2} .0514178{col 37}{space 1}   -3.01{col 46}{space 3}0.003{col 54}{space 4}-.2560146{col 67}{space 3}-.0539714
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.1720826{col 26}{space 2} .0590859{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4}-.2881697{col 67}{space 3}-.0559954
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0945588{col 26}{space 2} .0601834{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-.2128022{col 67}{space 3} .0236847
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0473016{col 26}{space 2} .0752793{col 37}{space 1}   -0.63{col 46}{space 3}0.530{col 54}{space 4}-.1952043{col 67}{space 3} .1006012
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.1238272{col 26}{space 2} .0446593{col 37}{space 1}   -2.77{col 46}{space 3}0.006{col 54}{space 4}-.2115703{col 67}{space 3}-.0360842
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.1099147{col 26}{space 2} .0573285{col 37}{space 1}   -1.92{col 46}{space 3}0.056{col 54}{space 4}-.2225492{col 67}{space 3} .0027197
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0484307{col 26}{space 2} .0744719{col 37}{space 1}   -0.65{col 46}{space 3}0.516{col 54}{space 4}-.1947471{col 67}{space 3} .0978857
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3704858{col 26}{space 2} .1015832{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .1709032{col 67}{space 3} .5700683
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est6{txt} stored)

{com}. estadd local sample "Unprimed", replace

{txt}added macro:
             e(sample) : "{res:Unprimed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:518}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Unaffiliated"

{txt}added macro:
             e(middle) : "{res:Unaffiliated}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==1 & order==1, robust
{txt}{p 0 6 2}note: {bf:r3} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       780
                                                {txt}F(16, 763)        =  {res}     1.93
                                                {txt}Prob > F          = {res}    0.0156
                                                {txt}R-squared         = {res}    0.0386
                                                {txt}Root MSE          =    {res} .34312

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.1219203{col 26}{space 2} .0399882{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.2004201{col 67}{space 3}-.0434204
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}-.0484523{col 26}{space 2} .0368431{col 37}{space 1}   -1.32{col 46}{space 3}0.189{col 54}{space 4}-.1207782{col 67}{space 3} .0238736
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0139554{col 26}{space 2} .0373078{col 37}{space 1}   -0.37{col 46}{space 3}0.708{col 54}{space 4}-.0871936{col 67}{space 3} .0592828
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}-.0030362{col 26}{space 2} .0366378{col 37}{space 1}   -0.08{col 46}{space 3}0.934{col 54}{space 4} -.074959{col 67}{space 3} .0688867
{txt}{space 6}female {c |}{col 14}{res}{space 2} .0439046{col 26}{space 2} .0269363{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0089734{col 67}{space 3} .0967826
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0001266{col 26}{space 2} .0013238{col 37}{space 1}    0.10{col 46}{space 3}0.924{col 54}{space 4}-.0024722{col 67}{space 3} .0027253
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0220113{col 26}{space 2} .0106046{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-.0428289{col 67}{space 3}-.0011937
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0133754{col 26}{space 2} .0360297{col 37}{space 1}    0.37{col 46}{space 3}0.711{col 54}{space 4}-.0573538{col 67}{space 3} .0841045
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0021013{col 26}{space 2}  .004927{col 37}{space 1}    0.43{col 46}{space 3}0.670{col 54}{space 4}-.0075708{col 67}{space 3} .0117733
{txt}{space 7}islam {c |}{col 14}{res}{space 2}  .062361{col 26}{space 2} .1230326{col 37}{space 1}    0.51{col 46}{space 3}0.612{col 54}{space 4}-.1791615{col 67}{space 3} .3038835
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.1710144{col 26}{space 2} .0563578{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-.2816491{col 67}{space 3}-.0603797
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r4 {c |}{col 14}{res}{space 2}-.0925725{col 26}{space 2}  .049142{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4} -.189042{col 67}{space 3} .0038971
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}-.0939478{col 26}{space 2} .0529998{col 37}{space 1}   -1.77{col 46}{space 3}0.077{col 54}{space 4}-.1979906{col 67}{space 3} .0100951
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}-.0866034{col 26}{space 2} .0413166{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4} -.167711{col 67}{space 3}-.0054957
{txt}{space 10}r7 {c |}{col 14}{res}{space 2}-.0745194{col 26}{space 2} .0502079{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-.1730814{col 67}{space 3} .0240426
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0799839{col 26}{space 2} .0577732{col 37}{space 1}   -1.38{col 46}{space 3}0.167{col 54}{space 4}-.1933972{col 67}{space 3} .0334294
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8102877{col 26}{space 2} .1454815{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} .5246962{col 67}{space 3} 1.095879
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est7{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:780}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Erdoğan"

{txt}added macro:
             e(middle) : "{res:Erdoğan}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==2 & order==1, robust
{txt}{p 0 6 2}note: {bf:r5} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       618
                                                {txt}F(16, 601)        =  {res}     2.71
                                                {txt}Prob > F          = {res}    0.0004
                                                {txt}R-squared         = {res}    0.0722
                                                {txt}Root MSE          =    {res} .23869

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2}-.0513608{col 26}{space 2} .0260613{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4} -.102543{col 67}{space 3}-.0001786
{txt}{space 10}t3 {c |}{col 14}{res}{space 2}  .070258{col 26}{space 2} .0338804{col 37}{space 1}    2.07{col 46}{space 3}0.039{col 54}{space 4} .0037197{col 67}{space 3} .1367962
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} -.010056{col 26}{space 2}  .026728{col 37}{space 1}   -0.38{col 46}{space 3}0.707{col 54}{space 4}-.0625476{col 67}{space 3} .0424356
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0430855{col 26}{space 2} .0320629{col 37}{space 1}    1.34{col 46}{space 3}0.180{col 54}{space 4}-.0198835{col 67}{space 3} .1060545
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0320713{col 26}{space 2} .0193117{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4} -.069998{col 67}{space 3} .0058554
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0025434{col 26}{space 2} .0008841{col 37}{space 1}   -2.88{col 46}{space 3}0.004{col 54}{space 4}-.0042797{col 67}{space 3}-.0008071
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0149356{col 26}{space 2} .0105767{col 37}{space 1}   -1.41{col 46}{space 3}0.158{col 54}{space 4}-.0357073{col 67}{space 3} .0058361
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2} .0282266{col 26}{space 2} .0286276{col 37}{space 1}    0.99{col 46}{space 3}0.325{col 54}{space 4}-.0279958{col 67}{space 3}  .084449
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0022867{col 26}{space 2} .0040247{col 37}{space 1}   -0.57{col 46}{space 3}0.570{col 54}{space 4} -.010191{col 67}{space 3} .0056175
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .0608082{col 26}{space 2} .0247944{col 37}{space 1}    2.45{col 46}{space 3}0.014{col 54}{space 4} .0121139{col 67}{space 3} .1095024
{txt}{space 10}r2 {c |}{col 14}{res}{space 2}-.0366398{col 26}{space 2} .0645927{col 37}{space 1}   -0.57{col 46}{space 3}0.571{col 54}{space 4}-.1634946{col 67}{space 3}  .090215
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}-.0844409{col 26}{space 2} .0668168{col 37}{space 1}   -1.26{col 46}{space 3}0.207{col 54}{space 4}-.2156636{col 67}{space 3} .0467819
{txt}{space 10}r4 {c |}{col 14}{res}{space 2}-.0795875{col 26}{space 2} .0638531{col 37}{space 1}   -1.25{col 46}{space 3}0.213{col 54}{space 4}-.2049898{col 67}{space 3} .0458148
{txt}{space 10}r5 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r6 {c |}{col 14}{res}{space 2}-.0629849{col 26}{space 2}   .06151{col 37}{space 1}   -1.02{col 46}{space 3}0.306{col 54}{space 4}-.1837856{col 67}{space 3} .0578158
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} -.070504{col 26}{space 2} .0656645{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-.1994637{col 67}{space 3} .0584558
{txt}{space 10}r8 {c |}{col 14}{res}{space 2}-.0794404{col 26}{space 2}  .069636{col 37}{space 1}   -1.14{col 46}{space 3}0.254{col 54}{space 4}-.2161999{col 67}{space 3} .0573192
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3175486{col 26}{space 2} .0896917{col 37}{space 1}    3.54{col 46}{space 3}0.000{col 54}{space 4} .1414014{col 67}{space 3} .4936958
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est8{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:618}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Opposition"

{txt}added macro:
             e(middle) : "{res:Opposition}"

{com}. eststo: reg o1_std t2-t5 female age education govt_emp income islam r2-r8 if partisan==3 & order==1, robust
{txt}{p 0 6 2}note: {bf:r3} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}       503
                                                {txt}F(16, 486)        =  {res}     1.84
                                                {txt}Prob > F          = {res}    0.0241
                                                {txt}R-squared         = {res}    0.0498
                                                {txt}Root MSE          =    {res} .32428

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      o1_std{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t2 {c |}{col 14}{res}{space 2} .0091322{col 26}{space 2} .0485957{col 37}{space 1}    0.19{col 46}{space 3}0.851{col 54}{space 4}-.0863514{col 67}{space 3} .1046157
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} .0146971{col 26}{space 2}   .04843{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4}-.0804609{col 67}{space 3} .1098551
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}-.0145399{col 26}{space 2} .0471176{col 37}{space 1}   -0.31{col 46}{space 3}0.758{col 54}{space 4}-.1071191{col 67}{space 3} .0780394
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} .0165213{col 26}{space 2} .0484539{col 37}{space 1}    0.34{col 46}{space 3}0.733{col 54}{space 4}-.0786837{col 67}{space 3} .1117264
{txt}{space 6}female {c |}{col 14}{res}{space 2}-.0091784{col 26}{space 2} .0307359{col 37}{space 1}   -0.30{col 46}{space 3}0.765{col 54}{space 4}  -.06957{col 67}{space 3} .0512133
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0027343{col 26}{space 2} .0012146{col 37}{space 1}   -2.25{col 46}{space 3}0.025{col 54}{space 4}-.0051208{col 67}{space 3}-.0003478
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0066284{col 26}{space 2} .0146231{col 37}{space 1}    0.45{col 46}{space 3}0.651{col 54}{space 4}-.0221039{col 67}{space 3} .0353607
{txt}{space 4}govt_emp {c |}{col 14}{res}{space 2}-.0633923{col 26}{space 2} .0360191{col 37}{space 1}   -1.76{col 46}{space 3}0.079{col 54}{space 4}-.1341646{col 67}{space 3}   .00738
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0096827{col 26}{space 2} .0059668{col 37}{space 1}   -1.62{col 46}{space 3}0.105{col 54}{space 4}-.0214066{col 67}{space 3} .0020413
{txt}{space 7}islam {c |}{col 14}{res}{space 2} .1322118{col 26}{space 2} .0453956{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0430159{col 67}{space 3} .2214077
{txt}{space 10}r2 {c |}{col 14}{res}{space 2} .0594093{col 26}{space 2} .0587721{col 37}{space 1}    1.01{col 46}{space 3}0.313{col 54}{space 4}-.0560695{col 67}{space 3} .1748881
{txt}{space 10}r3 {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}r4 {c |}{col 14}{res}{space 2} .0212296{col 26}{space 2}   .06485{col 37}{space 1}    0.33{col 46}{space 3}0.744{col 54}{space 4}-.1061914{col 67}{space 3} .1486506
{txt}{space 10}r5 {c |}{col 14}{res}{space 2} .1202504{col 26}{space 2} .0772562{col 37}{space 1}    1.56{col 46}{space 3}0.120{col 54}{space 4}-.0315469{col 67}{space 3} .2720477
{txt}{space 10}r6 {c |}{col 14}{res}{space 2}  .053621{col 26}{space 2}  .055078{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0545994{col 67}{space 3} .1618414
{txt}{space 10}r7 {c |}{col 14}{res}{space 2} .1100679{col 26}{space 2} .0651864{col 37}{space 1}    1.69{col 46}{space 3}0.092{col 54}{space 4}-.0180141{col 67}{space 3} .2381499
{txt}{space 10}r8 {c |}{col 14}{res}{space 2} .0275841{col 26}{space 2} .0695893{col 37}{space 1}    0.40{col 46}{space 3}0.692{col 54}{space 4} -.109149{col 67}{space 3} .1643172
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2666697{col 26}{space 2} .1004298{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} .0693395{col 67}{space 3} .4639999
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est9{txt} stored)

{com}. estadd local sample "Primed", replace

{txt}added macro:
             e(sample) : "{res:Primed}"

{com}. estadd local i `e(N)'

{txt}added macro:
                  e(i) : "{res:503}"

{com}. estadd local controls "Yes"

{txt}added macro:
           e(controls) : "{res:Yes}"

{com}. estadd local middle "Unaffiliated"

{txt}added macro:
             e(middle) : "{res:Unaffiliated}"

{com}. esttab using "`drive'/HTEmiddlefull.tex", replace ///
>         b(2) se(2) nomtitles label ///
>         booktabs ///
>         star(+ 0.10 * 0.05 ** 0.01 *** 0.001)   ///
>         longtable ///
>         s(sample middle i, label("Sample" "Partisanship" "Observations")) ///
>         title("Treatment Effects Conditional on Partisanship \label{c -(}tab:HTEmiddlefull{c )-}"\centering)
{res}{txt}(output written to {browse  `"/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/HTEmiddlefull.tex"'})

{com}. 
. 
. 
. *******************
. *** Close log file
. *******************
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/egoldring/Dropbox/Apaydin, Goldring, and Schmid/Data/Replication Files/GSA_PoP_Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}10 Jun 2025, 11:54:26
{txt}{.-}
{smcl}
{txt}{sf}{ul off}